{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5008b803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import copy\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "# 防止内核挂掉\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d266a80",
   "metadata": {},
   "source": [
    "# Angular Spectrum Propagation (phase & amplitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "351f8955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('Using Device: ',device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3966ea17",
   "metadata": {},
   "source": [
    "### ***Loading and Viewing Dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "414a3ef3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [100/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [200/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [300/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCH_SIZE = 200\n",
    "IMG_SIZE = 32\n",
    "N_pixels = 64\n",
    "PADDING = (N_pixels - IMG_SIZE) // 2  # 避免边缘信息丢失\n",
    "\n",
    "# 数据预处理并加载\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "                                transforms.Pad([PADDING, PADDING], fill=(0), padding_mode='constant'),\n",
    "#                                 transforms.Normalize((0.1307,), (0.3081,))\n",
    "                               ]\n",
    "                               )\n",
    "train_dataset = torchvision.datasets.MNIST(\"./data\", train=True, transform=transform, download=True)\n",
    "val_dataset = torchvision.datasets.MNIST(\"./data\", train=False, transform=transform, download=True)\n",
    "train_dataloader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_dataloader = DataLoader(dataset=val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# 定义一个绘图函数\n",
    "def image_plot(image, label):\n",
    "#     cmap='RdBu'\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.imshow(np.round(image.cpu().numpy(), 5)) # 显示图片每个像素点的振幅\n",
    "    ax.axis('off')\n",
    "    ax.set_title(label.cpu().numpy())\n",
    "#     fig.colorbar(plt.cm.ScalarMappable(cmap=cmap))\n",
    "    plt.show()\n",
    "\n",
    "for i, (images, labels) in enumerate(train_dataloader):\n",
    "    images = images.to(device)\n",
    "#     images_E = torch.sqrt((torch.squeeze(images)+1)/2)\n",
    "    images_E = torch.sqrt(torch.squeeze(images))\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    '''\n",
    "        每一个周期，共300个批次（i=0~299），一共60000个数据；\n",
    "        train_dataloader包含300个批次，包括整个训练集；\n",
    "        每一批次一共200张图片，对应200个标签, len(images[0])=1；\n",
    "        images包含一个批次的200张图片（image[0].shape=torch.Size([1,28,28])），labels包含一个批次的200个标签，标签范围为0~9\n",
    "    '''\n",
    "\n",
    "    #每100个批次绘制第一张图片（含shuffle导致每次运行结果不一样）\n",
    "    if (i + 1) % 100 == 0:\n",
    "        classes = torch.unique(labels).cpu().numpy()\n",
    "        classes_num = len(classes)\n",
    "        print('batch_number [{}/{}]'.format(i + 1, len(train_dataloader)))\n",
    "        print('classes of the first batch: {}, number of classes: {}'.format(classes, classes_num))#  第一个batch的总类\n",
    "#         image_plot(images_phase[0], labels[0])\n",
    "        image_plot(images_E[0], labels[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "563cd417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f9b3c7",
   "metadata": {},
   "source": [
    "### ***Diffractive Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0bcfe12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Diffractive_Layer(torch.nn.Module):\n",
    "    # 模型初始化（构造实例），默认实参波长为532e-9，网格总数50，网格大小20e-6，z方向传播0.002。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.02])):\n",
    "        super(Diffractive_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd1e4ca2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fXeX8TM//i7dE2V2VT4j96b7sMc9T2hCx2vCH8n0LHra3/1fn10TZjB+f4YKz8J97rrNjDB1Mn3YCx25xWsoN6w4MfxhsRpH2vjMNIVZusX6tzDifL6zldQSMfwva36cy80iKnTyP1BQZAksVO8fpP8eYQv0c/TMZp5OpI0jrC+d2yxeHtNH2Rd0yl0Ff8bZjgfwd6o25/CyO6yc38iO0z6/neHx8cc7nVebPWrunGXkzLqFDy/x5au91zdwwf5/Ez97ls3uYD5JT42M4U4JYloWGhgY0NDSM9JqIqMCw7iAiHdd2ISIiIk9xVVty5Z861d7uuHyaKEte60zq9H8/9ZQo+21Cdg+ve3u1vd3eJWcx1V19XqvYrwo7faDpfmP1R60r02d0zevD11S5PF/vBc5nsoyFIcOvvGNvpxh2obORy3Ba/W1GKFB0oZtlGafUuv7N3iY9fDIgpwO1tJABjLCLVaINpy2Sf9epMqcseY6xcm2Jc92JqZZR5mwXn5TXGeiVl60Cznv9xp+k/vfbN0Oeo6fC+RyDJfIcqWJnP9hlhF5dIltWiXPhaWNosXjdgMsQWXMos/lePbTiEo7LicvstsbJs5eNckiGPR9ERETkKTY+iIiIyFNsfBAREZGnmPNBrgYWzbO3266TccxHl/zE3o6nZfLEf9t3m9iP/puTgxH7oxHU1bxy0WKxv3ehtlMmz6HneQwsnCfKuiud8526WMYxL77Wyet49/RUURbeqO2fOJn1OonOyC1G7hJbV8bwTsunxejT7t8XlT4A01yBVs/lMHIJ0v1OMoVfmzL94+Norw3KfxmpUidBot+YplyfJn3QGJWaLnKuMx2U19lbIT///OXO3+u5Ibki9kf9Th3wdvsMUZZs1xJLjB+FNeic00oZ90l7bbpYfiZV5hxTFRm5MQNa/eQ3fk7a/TaHMg+1pICz45af4TN2ndeav0MjNoZDb9nzQURERJ5i44OIiIg8xbALuUqc43Srlk7tEWVVQaeb7/l+Gb6Y9v9k1+3UF52ZSwdb3816vmmpS8X+qUXOSrpfWvaSKDv0qLMA2eXlB0TZ/PAxe/sHRz4jyg5/6Kycm/pI9gdX9L6f9dqIcjLCobZnRe+mN4aRiw50lxkw093y79ynfw4jnKDPImrOVKq0/y7BHqNMCwkpY9LUgagcBrxh9l57uzYswxA/65pub/9D++dEmb9XWx3Wb4SytFCLzxwVq51ioFT+iwyUOMNr0wF5L/xa2MVKDIgypYelfS5hFkDMqqpSaffXZmEO1xZhGLfZTzMOpB2HQ22JiIgon7HxQURERJ5i44OIiIg8xZwPcqX8Tswv6JdB5BLLiX+e6+8SZT0VMuaYmq5Nfy5nUMep/77c3j65Qs6ffOvlv7G3v3aOzPn4KOLka+zvnyfKtrxWZ2+HXpLTq0dOObHL4k4jptoZB9GoMGPkw8wByZhe3fUc5qqn2pBOc7hnUJs3PG28z+2cSSd/weqTf5+Bjj57u9jIj7DSTjKHuVLtwBTnfP3Tjfuk5LW82jfP3o6n20TZzz5cZm/7fi+XWCjWqqTENCMHQtv1yfQMkYOSLpLvS57jDOEPdhtv1O6pMu+9nnNhrAycMdQ2rSWhpIzEHbchtMOdQt2tLOO1HGpLREREBYKNDyIiIvIUwy7kyko53W7JQdldOKCcLsHKgOyC7L+oX+x3veWEPo7/+XJRVnXlUXv7r2f/VpTVhJ2yfqM7dl9vtb39Ty/+iSg77wnnWiO//Q9RNtj2EbJJZS0hOktuXdja35JSLiEQy5z91JidU+vut4qMWEdR9uVaLW1Ip74iNCBXhU6VGGUh5+/Ml5Td+f6kcy0pI3yR0kbiJ6cZf3X98jvxvQdW2NvpPvkv65zfO5/pvIN9ouzDGicsO1gs71uwRxtq67J4dTpghES04bXpkKwP/ae08FS/HC4szm6seJvxW6GVKzPsoodMRmvFW3H8sQuzmNjzQURERJ5i44OIiIg8xcYHEREReYo5H+Sq+LgTEO09XirKXkk6MeVlIRkbveWyF8X+o+WL7O0/m/uGKPt85NWs5/+XTmeV23//cKEoe//tmfb2/B93irL0a3+wt83Zk4nyVsYwSWNucn0FWmNIp8gRGDTyDrR9KxwRZSltSPtgmcwjGShzztEfMXLCyrKvapuMOJ+jvEIO0/cZeS2dHU69U/yBzFsJn3CO0zvTyHHRBPpl/eTvy/JCAJaeVpEyhwEPMydiwBy/6zK81cjrEHkeuaxOq8ZgVdsxxJ4PIiIi8hQbH0REROQphl3IlbXv9/Z2+ZU1ouxHlzqrxV5b9WtR9rfT94v9z5f/zt7uUbJ79NW+Knv750eXiLLjr0Tt7bm/lH2l1b91Qjs5zNlHNP7cZjs1hlCK4bRnGF4puuyTcrinpXfLG6ulihlPjdCCPrw0WS7/ZfSe65T1nWvMBjrNOWaq1BiGW+aEJYoCMuyQMIb0q4Q+ZFdeW980p8yfkGVF2mTFlhGGCPQiO+1jmENt9TCMv1veX/EzNUJe4n6bw2fN3wW9PIeZSl1DLa7HGZ8QDXs+iIiIyFNsfBAREZGncmp87NixA4sWLUJ5eTnKy8uxfPly/PKXv7TLlVJoaGhARUUFwuEwamtrcejQoVG/aCLKL6w7iEiXU87H7Nmzcc899+CCCy4AAOzevRs33XQTfve73+GSSy7Bli1bsHXrVjzwwAO48MILcffdd2PlypU4cuQIysrKznB0moh8l11sb5sx1T+cdIa6okoUYYqvWOxXBnrs7f/5zudF2dt7z7e3Zz/dI8rmPf98TtdLExPrjrGRsXKtPtTWl/27pbmSKrSp2FXAyFfQzqGM0btKP50xpt034Jwj5Zd1R6jYyZeoLO8QZVOCcr7zNwIxe/tksVyhOl7iXHfJB/LzBru01atPm9OrOzkQ6aC8F74B57XmlPE6cxiuFe/O/tqA86821S1flzENvouM6dbFgYa5qu0EkVPPx4033ojPfe5zuPDCC3HhhRfie9/7HqZMmYIXXngBSils27YNmzdvxurVq7Fw4ULs3r0bvb292LNnT9ZjJhIJxONx8SCiwsK6g4h0I875SKVSeOihh9DT04Ply5ejtbUVbW1tqKurs18TCoWwYsUK7Nu3L+txmpqaEIlE7EdlZeVIL4mI8gDrDiLKeajtwYMHsXz5cvT392PKlCl49NFHsWDBAruSiEaj4vXRaBTvvvtu1uNt2rQJGzZssPfj8TgrkXHkv+Qisf9B7VR7u2jlCVH2Twv+RX+n63EPJ52u80P754my+f/8jr09eKxtmFdK+WZS1B1uQ2hHSA6hdF932dKGeOpd/QAAfd9cDVdbyTYVKRFlgyXOMTOGs2qhDZgjRsPOE+kSGZPx+Zz3tXZMF2VlxTLsEgo47/UHZTghPd15bU9Qhi8CcW2I8Dnye/bUI875S96X427FCr8DxuyjQa2eSxuhDe3+qm4ZPlbasGfLHIZrDok2Z6YVhS6hlXR+rcmdc+PjoosuwoEDB3D69Gk8/PDDuPXWW9HS0mKXm7FEpVRmfFETCoUQCoWylhNRYWDdQUSfyDnsUlRUhAsuuABLly5FU1MTLrvsMvzgBz9ALPZxUlBbm/zm2t7envGNhogmH9YdRPSJs57nQymFRCKBqqoqxGIxNDc322XJZBItLS2oqalxOQIRTUasO4gmr5zCLn/3d3+HVatWobKyEl1dXXjooYfwzDPP4Mknn4RlWaivr0djYyOqq6tRXV2NxsZGlJSUYM2aNWN1/TTKjvyPqWL/Lz7jJPzdNv05UTbD58RY3xzoF2UXBuUKuJcVOcPLSs+XK9B2XDvP3i77OXM+CtGkqTv0qapzyf/QYvmWkY8h4vxmroZ5DpepsvW8DlUs8yPSYWd/ICJDWQNTnBwEc1iq0q4nKNMckNRWQ+jvl3kM5dOd+iKt5DFP9cick2RCG6Z6Ul5bUYd231LyOEFtsVxjoVwxLXtRXN6LopPOhfu6Zb2GPm0/ZAyR1e6Ffq8/vnAnHyNtrChsGbkqYnVac2it2+/XSPONxml69ZwaHx999BFuueUWHDt2DJFIBIsWLcKTTz6JlStXAgA2btyIvr4+rF+/Hh0dHVi2bBn27t3LcfpEkxzrDiLS5dT4+MlPfuJablkWGhoa0NDQcDbXREQFhnUHEem4tgsRERF5KuehtpT/zLk8/rDRyc/45pJmUfYXZb+3t4PGce497Uy9ft9z14my7dc/KPZXFDux0oXnyryOg3OcPBN2slNeG27c3fIZu1b2Mn0KdXMOCJcp1N2osMwzGIyE7e2BMnmORLlzjsQ0I+dDe2m4XeYOlH7o7KeNKcTbQhF7e17spLyWlDx/f5/23rSZ1+Hs+430jECvc/5wR9ooc/YDXXKeDd9JbaZccy6PsLZsRJ88YbrTeZ9lDgHXfm6+Yrn0RDoh5zUZtnHK1Rgt7PkgIiIiT7HxQURERJ5i2GUS+sM3zxH7f33FE/b2TVPeEGUnUk6w5X+fWCHKmp9ebG9XPi+HhDXMlivX/p9L/9nevjkqV6q94xJnVVtctUhe7AuvgShvuHWF6yGZjFVHXabU1kIrGVNvm8Ny9anYjTIVcv6WxTThAJRfW4G2SH4nHZjilPWda6zkqn2MkFycFkFtKvbQKXkt/R1OKKWjPCzKlDH0Nn3aeW2gx1iBVhu1Gj4p72lR3Nn398v6KdCjTdl+Wk6vrk+Nnu7qEmW+6dPsbXM1WqvMWXFXaSEYQIZWrIAMYJv7rkNthxvWy4OQDHs+iIiIyFNsfBAREZGn2PggIiIiTzHno0AF5sqlxds+O9ve/rPlL4oyPc/j3UE5tfH33v2v9nZryzxR9ql/c+Kavr4BUfbBm9PEftdC51etJnRKlP35olft7cc+f5Uoq3oBRPkph+mulZ6rgeEvjW4FjCpczwlxKUuFZZ5BKqQv1W7kdWi7KiDLBkud/IS4JfNI/AnnmOmgfJ+a4uRcdLTLAfa+uLzu8HHnOKmwPE7/DGc/GZHfpYu190Va5ft8HVqyiDmcVsu5MO+vng+ifH2iTBzHyM3xlTj1qjKmV1cDch9p7efv9juUB3kdbtjzQURERJ5i44OIiIg8xbBLAdG79gZmTxdlyVXOSrK3Tt8nyvROx3uPyZlK33tqnr095x75Pr3Tz5o3R5SVtcrzt/Q4s6p+tVwO570h4syi+vinLgVRQTC7xUe86qjL0EuDPlOqZcx+qrSwgG9QhhrSQS1EEjCG6OojhM0RwUXacebJmTr7u5yhqP4pMix76XnOLMev/7FClEVlVBjh485xT10sZw7tn6HtGLc3We5sd58n/9VZaaeuDPfKGU6hz06aMkIyA87nMMMnPv195jBnLbRirkRsFckQmFLatRo/b/Hzz+X3aQKGaNjzQURERJ5i44OIiIg8xcYHEREReYo5HwXEKnGmKe6Lytjo5gWP2dsXBGSb8/EeZ1ju84cuEGUXPeXkirhFDdMn5dzKs56dIvZ//Jlr7O1li98WZcWWE0cNBoc/zJBo3OUSd7eyf9ezzGnSh/k+c5io0nMUjDJLKzOHxlspp74YLDZyErQ/SWVcStEUJ1+iOCSPmU47L54947Qo2zB7r7394rRPibKdp2Te2dRD2jTmxm3yaac0r82vpaAUdRlDfbXclb45EVEWmuKsOus/IadJV73O8ForbdSIA869SBsr3up5HsrIv8gYaqtPvW/+7PX9jCn69WNMvBwPE3s+iIiIyFNsfBAREZGnGHYpJPoMhkWyf3Je8IS9HTRmInwnMdPZScv3nVrkjFcrnblUlBU99YrzNmP1Rxw4LHZTB2qc810yU5SdX9TuXJtfhl38F1Q5x3i7FUSFICPMonenG2ViaKYxfDZjdk5zX6d1xVt9cnhpsMuJX/hmZP+3EOg1VpX1Ze/e13v+3/tDVJTtLHVWyE4bsZTwXFmXnJrqhEHQJa/Nl3Deq89oCgBFce3zGrfFN+CUFZ2SIRLfaW0W07i8FqWFU8whsvoQXZ8xw6n+PhhhFnOlYqVXgWZoJQ/CKcPFng8iIiLyFBsfRERE5Ck2PoiIiMhTzPkoIKrTGRZW9k6PKFvz7Dfs7T3/ZZco++bU39nbF/3JMVH2L5dcaW/vf/18UXZB72J7O9Alp1bGfxwVuyVtTqzy7YSM/y4IOeesPOe0KDu90Jm2PcycD5poRmsKdXEIczypTy/MXmZejzkUVGfkhujTrZtDSPUhq8Euef6+XifvIWEZORCdzv45b8jrfP3di+3t7ktl3fG5S14X+6eSzlTob5yQdcfp486QfnWySJSltctJG//pUkXO9QxOke8r0qdbN1a1tcJa/omRu5E+7UxLYOZxCEYeh3L7OZn0n3+e53+w54OIiIg8dVaNj6amJliWhfr6evs5pRQaGhpQUVGBcDiM2tpaHDp06Gyvk4gKBOsNIhpx2OXll1/Gzp07sWjRIvH8li1bsHXrVjzwwAO48MILcffdd2PlypU4cuQIysrKzvqCKbt0wum+9L/5niirvtcJX6x99Q5RVnfr8/b2nTOeFWWrqp6wt187T3YlbrnkT+3t3701V5TN/6dKsR976n17+8Ats0XZVyLOkN1FkQ9E2b/PdkI9YVC+Y73xMbOr3fLpK9eaL9aGjJ4prBPUYg3mcN60HloxZj9NOCf1GRNuisN3y/3ECSdkYa54G+x0vtsWdcnz6cNgu6rlGz/sKxf77b3Oz38gJV/r04beBuSIWSjtXvkGjVBS0rkef7/xgQe1H4AZEnFZVVgPtaT7jTC026ylbsNpRyGMN1GNqOeju7sbN998M3bt2oWpU6fazyulsG3bNmzevBmrV6/GwoULsXv3bvT29mLPnj1DHiuRSCAej4sHERWe0aw3ANYdRPlsRI2P2267DTfccAOuv/568Xxrayva2tpQV1dnPxcKhbBixQrs27dvyGM1NTUhEonYj8rKyiFfR0T5bTTrDYB1B1E+y7nx8dBDD+HVV19FU1NTRllbWxsAIBqVGcnRaNQuM23atAmdnZ324+jRo0O+jojy12jXGwDrDqJ8llPOx9GjR3HnnXdi7969KC4uzvo6My6plMoaqwyFQgiFQkOWUY60WGGqU3ZB+157y94+r0fmZzxZtNzePvXnpaLs7oon7e3Fxs/p3nmPOu+bI2Oxf1wxVeyntCUnL9WmUweA2QEnm8NvzINsrlRJ+Wcs6g1ggtQdI4zJu65ia9LyDlRGQohxXLchnjpjiK6l1R1F3fIcSs9lMEbTBrqd46iAsVqrNvV61xx5vqA2a3ngtLwXv39P5oSplFYelxdQ+oFzXMtI3Sg+7dQl4eNyOvlAtzad/Gk5LQE6tJW8tVVsASOXw8jVEPfedVp0lynwTXk+nNZNTlX7/v370d7ejiVLliAQCCAQCKClpQU//OEPEQgE7G8u5reV9vb2jG81RDQ5sN4gIlNOjY/rrrsOBw8exIEDB+zH0qVLcfPNN+PAgQM4//zzEYvF0NzcbL8nmUyipaUFNTU1LkcmokLFeoOITDmFXcrKyrBw4ULxXGlpKaZPn24/X19fj8bGRlRXV6O6uhqNjY0oKSnBmjVrRu+q6cyM7rp0vzYO7dARUVaJi+ztF31yCOR1V8yzt6+fJ9/3J5E37O0vlMoxePODvVkvrcNYOff+uJMo+NODV4qyuYeNIWuUd1hvjCJjmKYyh34OOrEHKyH/dvQawSqSs3rqfAlzWKw+ZFWePxVyygamGGGzoP464yTaxQT65PsG35ehOT0SGzJCND7t4/uTss4L9mpDi42httaA9kYjdGZps5oqc0hy0ClzDXEZPxelz4Z6ppVq3UJ5BRSGGfXp1Tdu3Ii+vj6sX78eHR0dWLZsGfbu3VuQY/WJaHSw3iCaXM668fHMM8+Ifcuy0NDQgIaGhrM9NBEVKNYbRJMbxxIQERGRp7iqLSGl5YDMaT9XlMXfdqY3/9WCK0TZL85zVrVtrOwY9vl6EzLe3Pee07U+41UZ7ww9/5q9ncMANSJvuMXgzdi9FutX6ezf+/Sp1ocoNHZd8gNcpgZXSTn01OpxcsKCxfLfgj41uW9Anr/Ucl7bN12WDWizpJtDdH3OSFexai4AFHUaORja7TBXp9WH1+pTtgNAsMv5vP5e+XnFVPPJAVnUkz1fTc/zyMi30e9vxvT5llZm/uxzqNm4qi0RERHRyLDxQURERJ5i2IWE1PHjYr/04ePa9uicY+qZX2JjqIXyllu3uDlTqdad7jqJqZW9O/+MtJCBZcxwCm0oqK/bCMlow1L9/XJ4qX45PmPF2USfc46UMbGtHnYp6jI+k3HbBsLaZzQ+bqjTqSGCXXKK02CnE8/RV+39eN/5jKpPLoerz6qrjOG0ShvKnBHW0u+v8SEyQjTihC6r3OZ5aMUNez6IiIjIU2x8EBERkafY+CAiIiJPMeeDiGi8DXfIrjk1N4wpvvUVcI08A0vPVzCnV09pQ0/75dhXK6l9RzWu09fnzJvuT8jEDn+/M752MGwMn9UOYxnpEOZK1kU9zosDffLz61PBB+NGrkpSm2reuG59eK0yhtrq903keACZeR7DJIbamvkfBZzX4YY9H0REROQpNj6IiIjIUwy7EBFNJG6rmprDMt2YK6vq4QWz619bydUakGEIMQzYWOXV1+eEaIID8pg+LQyTMmZNVXoYwhxp6jdWrtVnWE3K8/v7tdBKn7xuS/uMVnefPEd3j7Nthlb0e2PeJ/3+GyEwNaCyllEm9nwQERGRp9j4ICIiIk+x8UFERESeYs4HEdF4G2aexxmnU3cr14eQmrkM+tBTY0rxbMcAIHNFjNwJv3ZMX0lIlKmQMwxXneEjWfow4EGZS6EPp9WHC39cpn2mPiPnQ1vVN5fhtK7TpLuZpMNp3bDng4iIiDzFxgcRERF5imEXIiKvuYVZXKj0GVa11cv9ZpkTllBGiEIMDTXDLi6zpkI54Qxf0phhVAtn+FJT5Nu08IkKuoR5ABGyMMMu4nwDMnyierRQizGLqetw2tGQMSR6cqxUmwv2fBAREZGn2PggIiIiT7HxQURERJ5izgcRkdfMuL9bDojLVN0qbX5/HF7+QkbuRlrbN/NKtBwQX7EcMivySMwhq9oU5mYOhJ6fYRUFRZnyu3wnNlen1fNReo3htP39zraRj6LvW25Di92YPxfmcuSEPR9ERETkqZwaHw0NDbAsSzxisZhdrpRCQ0MDKioqEA6HUVtbi0OHDo36RRNRfmHdQUS6nHs+LrnkEhw7dsx+HDx40C7bsmULtm7diu3bt+Pll19GLBbDypUr0dXVNaoXTUT5h3UHEX0i55yPQCAgvrF8QimFbdu2YfPmzVi9ejUAYPfu3YhGo9izZw/Wrl179ldLRHmLdYeL4eYLmLkhbvkgZvqHcplrQj9uRi6DNfTrACDo5GtYPvldNq1NaW6ZuRr6tOxmHklA/luyXPJh9DwTpeeYmGVmPopPy/PImJNDO8ZYzAFCAEbQ8/HWW2+hoqICVVVV+Mu//Eu88847AIDW1la0tbWhrq7Ofm0oFMKKFSuwb9++rMdLJBKIx+PiQUSFh3UHEX0ip8bHsmXL8OCDD+Kpp57Crl270NbWhpqaGpw8eRJtbW0AgGg0Kt4TjUbtsqE0NTUhEonYj8rKyhF8DCKayFh3EJEup7DLqlWr7O1LL70Uy5cvx6c+9Sns3r0bV111FYDMLjKllGu32aZNm7BhwwZ7Px6PsxIhKjCsO0ZJTsM5XYaCmvdVDz0YYRd9SnczfKIPtTVZRUXOMQZcVo41j2kOfdWu1QyDqEQia5k4hBHKEecwV7F1CWW5hq7ECc3wGIfhms5qqG1paSkuvfRSvPXWW3Ys1/ym0t7envGNRhcKhVBeXi4eRFTYWHcQTW5n1fhIJBJ44403MGvWLFRVVSEWi6G5udkuTyaTaGlpQU1NzVlfKBEVDtYdRJNbTmGXv/mbv8GNN96IOXPmoL29HXfffTfi8ThuvfVWWJaF+vp6NDY2orq6GtXV1WhsbERJSQnWrFkzVtdPRHmAdQcR6XJqfLz//vv4yle+ghMnTuDcc8/FVVddhRdeeAFz584FAGzcuBF9fX1Yv349Ojo6sGzZMuzduxdlZWVjcvFElB9Yd0wAeh6Cy/DSjDItz8HM3bBcpkLX83XMKdShD8s1cjyGyv2xmTkfej6KlmMCIDOXY5iUyEcZpSnUxVBm5n8AgKXUxLoT8XgckUgEtbgJASt45jcQ0agbVAN4Bo+hs7Mzb3IpWHcY3NaLcWt8mLR/wFZA3le3xoerkTY+BgZEWTrp7FtB47u0W+PDZ2V9nUhcHWnjw3Wtngn1L3dU5VJvcG0XIiIi8hRXtSUimmzMb/QuQ23F29xm/PTJb/tuw6RdewaM4yCVvadA7+3I6DGBSw+Da2hlmMNp6ayw54OIiIg8xcYHEREReYqNDyIiIvIUcz6IiAqR28q1Ga91m1JcP44x9bqWAmIpI+dCG8WSkf+hH9Ocot3tOkfKLY8lY3r1UcjzYK7IGbHng4iIiDzFxgcRERF5imEXIiIanoxwgjb7aVp+l7Us57XK+Jpr6aEWnyzMGM6rndNtTsyMMn1mVrcJx9xCTjRm2PNBREREnmLjg4iIiDzFxgcRERF5ijkfRESTwVgM/3QZhgt96K2RDyKmPjeuy3Va9oxhscPM12Bex4TDng8iIiLyFBsfRERE5CmGXYiIaNTpw1stn0vYwy0kk3nQrOfICWcgHXfs+SAiIiJPsfFBREREnmLjg4iIiDzFnA8iIjp7OUy9rnPNBzFPkUuOh54fwhyPCYc9H0REROQpNj6IiIjIUwy7EBHR2DJnGLV8WtFZhEQ4c2neYs8HEREReSrnxscHH3yAr371q5g+fTpKSkpw+eWXY//+/Xa5UgoNDQ2oqKhAOBxGbW0tDh06NKoXTUT5h3UHEX0ip8ZHR0cHrr76agSDQfzyl7/E4cOH8Y//+I8455xz7Nds2bIFW7duxfbt2/Hyyy8jFoth5cqV6OrqGu1rJ6I8wbqDiHQ55Xx8//vfR2VlJe6//377uXnz5tnbSils27YNmzdvxurVqwEAu3fvRjQaxZ49e7B27drRuWoiyiusO0hwy9WwXL4TM8ejYOTU8/H4449j6dKl+OIXv4iZM2di8eLF2LVrl13e2tqKtrY21NXV2c+FQiGsWLEC+/btG/KYiUQC8XhcPIiosLDuICJdTo2Pd955Bzt27EB1dTWeeuoprFu3Dt/61rfw4IMPAgDa2toAANFoVLwvGo3aZaampiZEIhH7UVlZOZLPQUQTGOsOItLl1PhIp9P49Kc/jcbGRixevBhr167FN77xDezYsUO8zrIssa+UynjuE5s2bUJnZ6f9OHr0aI4fgYgmOtYdk5BSziOn96WzP9zOYT5oQsup8TFr1iwsWLBAPHfxxRfjvffeAwDEYjEAyPim0t7envGN5hOhUAjl5eXiQUSFhXUHEelyanxcffXVOHLkiHjuzTffxNy5cwEAVVVViMViaG5utsuTySRaWlpQU1MzCpdLRPmIdQcR6XIa7fJXf/VXqKmpQWNjI770pS/hpZdews6dO7Fz504AH3eZ1tfXo7GxEdXV1aiurkZjYyNKSkqwZs2aMfkARDTxse4gIl1OjY8rrrgCjz76KDZt2oTvfve7qKqqwrZt23DzzTfbr9m4cSP6+vqwfv16dHR0YNmyZdi7dy/KyspG/eKJKD+w7iAinaXUxMrMicfjiEQiqMVNCFjB8b4coklpUA3gGTyGzs7OvMmlYN0xgWVJGj5rbv++9HNOrH9zBSuXeoNruxAREZGnuKotERHlh1x6MNjbMaGx54OIiIg8xcYHEREReWrChV0+yX8dxADAXjOicTGIAQDO32M+YN0xkY1Swmke/T5ORrnUGxOu8fHJ8tnP4YlxvhIi6urqQiQSGe/LGBbWHRMY2wyTynDqjQk31DadTuPDDz+EUgpz5szB0aNH82aon1fi8TgqKyt5bwy8L9nlem+UUujq6kJFRQV8vvyIzrLucMe/j+x4b4Y2lvXGhOv58Pl8mD17tr08NtdsyI73Zmi8L9nlcm/ypcfjE6w7hof3JTvem6GNRb2RH19piIiIqGCw8UFERESemrCNj1AohO985zsIhULjfSkTDu/N0HhfsptM92YyfdZc8L5kx3sztLG8LxMu4ZSIiIgK24Tt+SAiIqLCxMYHEREReYqNDyIiIvIUGx9ERETkKTY+iIiIyFMTtvFx3333oaqqCsXFxViyZAmeffbZ8b4kTzU1NeGKK65AWVkZZs6ciS984Qs4cuSIeI1SCg0NDaioqEA4HEZtbS0OHTo0Tlc8PpqammBZFurr6+3nJvN9+eCDD/DVr34V06dPR0lJCS6//HLs37/fLi/0e8N6g/XGcLHucIxLvaEmoIceekgFg0G1a9cudfjwYXXnnXeq0tJS9e677473pXnms5/9rLr//vvV66+/rg4cOKBuuOEGNWfOHNXd3W2/5p577lFlZWXq4YcfVgcPHlRf/vKX1axZs1Q8Hh/HK/fOSy+9pObNm6cWLVqk7rzzTvv5yXpfTp06pebOnau+/vWvqxdffFG1traqX/3qV+rtt9+2X1PI94b1BuuN4WLd4RivemNCNj6uvPJKtW7dOvHc/Pnz1V133TVOVzT+2tvbFQDV0tKilFIqnU6rWCym7rnnHvs1/f39KhKJqB/96EfjdZme6erqUtXV1aq5uVmtWLHCrkAm83359re/ra655pqs5YV+b1hvZGK9kYl1hzRe9caEC7skk0ns378fdXV14vm6ujrs27dvnK5q/HV2dgIApk2bBgBobW1FW1ubuE+hUAgrVqyYFPfptttuww033IDrr79ePD+Z78vjjz+OpUuX4otf/CJmzpyJxYsXY9euXXZ5Id8b1htDY72RiXWHNF71xoRrfJw4cQKpVArRaFQ8H41G0dbWNk5XNb6UUtiwYQOuueYaLFy4EADsezEZ79NDDz2EV199FU1NTRllk/m+vPPOO9ixYweqq6vx1FNPYd26dfjWt76FBx98EEBh3xvWG5lYb2Ri3ZFpvOqNwMgveWxZliX2lVIZz00Wt99+O1577TU899xzGWWT7T4dPXoUd955J/bu3Yvi4uKsr5ts9wUA0uk0li5disbGRgDA4sWLcejQIezYsQNf+9rX7NcV8r0p5M+WK9YbEuuOoY1XvTHhej5mzJgBv9+f0aJqb2/PaHlNBnfccQcef/xx/PrXv8bs2bPt52OxGABMuvu0f/9+tLe3Y8mSJQgEAggEAmhpacEPf/hDBAIB+7NPtvsCALNmzcKCBQvEcxdffDHee+89AIX9O8N6Q2K9kYl1x9DGq96YcI2PoqIiLFmyBM3NzeL55uZm1NTUjNNVeU8phdtvvx2PPPIInn76aVRVVYnyqqoqxGIxcZ+SySRaWloK+j5dd911OHjwIA4cOGA/li5diptvvhkHDhzA+eefPynvCwBcffXVGcMq33zzTcydOxdAYf/OsN74GOuN7Fh3DG3c6o0Rp6qOoU+GzP3kJz9Rhw8fVvX19aq0tFT98Y9/HO9L88w3v/lNFYlE1DPPPKOOHTtmP3p7e+3X3HPPPSoSiahHHnlEHTx4UH3lK18p+GFhQ9Ez1pWavPflpZdeUoFAQH3ve99Tb731lvrZz36mSkpK1E9/+lP7NYV8b1hvsN7IFeuO8as3JmTjQyml7r33XjV37lxVVFSkPv3pT9tDxSYLAEM+7r//fvs16XRafec731GxWEyFQiF17bXXqoMHD47fRY8TswKZzPflF7/4hVq4cKEKhUJq/vz5aufOnaK80O8N6w3WG7lg3fGx8ag3LKWUGnm/CREREVFuJlzOBxERERU2Nj6IiIjIU2x8EBERkafY+CAiIiJPsfFBREREnmLjg4iIiDzFxgcRERF5io0PIiIi8hQbH0REROQpNj6IiIjIU2x8EBERkaf+PwlJUSyr4KX7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看看一个样本经过衍射层后变成啥样\n",
    "model = Diffractive_Layer().to(device)\n",
    "out = model(images_E)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.imshow(images_E[0].squeeze().cpu())\n",
    "ax2.imshow(torch.abs(out[0]).cpu())\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de95f338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(133.2661, device='cuda:0')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原图像总光强\n",
    "torch.pow(abs(images_E[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c8b23bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(133.2661, device='cuda:0', dtype=torch.float64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出图像总光强\n",
    "torch.pow(abs(out[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971a347f",
   "metadata": {},
   "source": [
    "### ***Propagation Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9cdc0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Propagation_Layer(torch.nn.Module):\n",
    "    # 与上面衍射层大致相同，区别在于传输层是最后一个衍射层到探测器层间的部分，中间可以自定义加额外的器件。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.001])):\n",
    "        super(Propagation_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e430c8",
   "metadata": {},
   "source": [
    "### ***Detectors Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "606fd717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x255d2cbbd00>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成一行探测器。指定探测器个数N_det，在x方向上生成齐高等间距det_step的一组探测器\n",
    "# left，right，up和down分别是该行矩形探测器的四个顶点坐标。\n",
    "def generate_det_row(det_size, start_pos_x, start_pos_y, det_step, N_det):\n",
    "    p = []\n",
    "    for i in range(N_det):\n",
    "        left = start_pos_x+i*(int(det_step)+det_size)\n",
    "        right = left + det_size\n",
    "        up = start_pos_y\n",
    "        down = start_pos_y + det_size\n",
    "        p.append((up, down, left, right))\n",
    "    return p\n",
    "\n",
    "# 生成三行探测器。利用generate_det_row函数生成三行等间距矩形探测器。\n",
    "def set_det_pos(det_size = 20, start_pos_x = 46, start_pos_y = 46, \n",
    "                N_det_sets = [3, 4, 3], det_steps_x = [5, 3, 5], det_steps_y = 5):\n",
    "    p = []\n",
    "    for i in range(len(N_det_sets)):\n",
    "        p.append(generate_det_row(det_size, start_pos_x, start_pos_y+i*(det_steps_y+1)\n",
    "                                  *det_size, det_steps_x[i]*det_size, N_det_sets[i]))\n",
    "    return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# def set_det_pos(det_size=20, start_pos_x = 46, start_pos_y = 46):\n",
    "#     p = []\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y, 2*det_size, 3))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+3*det_size, 1*det_size, 4))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+6*det_size, 2*det_size, 3))\n",
    "#     return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# 获取最终衍射光强在各个探测器上的分布情况\n",
    "def detector_region(Int):\n",
    "    detectors_list = []\n",
    "    total = 0\n",
    "    full_Int = Int.sum(dim=(1,2)) # 统计总光强\n",
    "    for det_x0, det_x1, det_y0, det_y1 in detector_pos: # 计算各个探测器区间内的光强占比\n",
    "        detectors_list.append((Int[:, det_x0 : det_x1, det_y0 : det_y1].sum(dim=(1, 2))/full_Int).unsqueeze(-1))\n",
    "    for ele in range(0, len(detectors_list)):\n",
    "        total = total + detectors_list[ele]\n",
    "    return torch.cat(detectors_list, dim = 1)/total\n",
    "\n",
    "# 指定生成的十个探测器的位置。\n",
    "# detector_pos = [\n",
    "#     (46, 66, 46, 66),\n",
    "#     (46, 66, 106, 126),\n",
    "#     (46, 66, 166, 186),\n",
    "#     (106, 126, 46, 66),\n",
    "#     (106, 126, 86, 106),\n",
    "#     (106, 126, 126, 146),\n",
    "#     (106, 126, 166, 186),\n",
    "#     (166, 186, 46, 66),\n",
    "#     (166, 186, 106, 126),\n",
    "#     (166, 186, 166, 186)\n",
    "\n",
    "# 定义探测器模型基本参数\n",
    "det_size = 4\n",
    "det_pad = (N_pixels - 13*det_size)//2\n",
    "detector_pos = set_det_pos(det_size, det_pad, det_pad)\n",
    "\n",
    "# 定义探测器层的图片张量\n",
    "labels_image_tensors=torch.zeros((10,N_pixels,N_pixels), device = device, dtype = torch.double)\n",
    "for ind, pos in enumerate(detector_pos):\n",
    "    pos_l, pos_r, pos_u, pos_d = pos\n",
    "    labels_image_tensors[ind, pos_l:pos_r, pos_u:pos_d] = 1 # 设置探测器区域\n",
    "    labels_image_tensors[ind] = labels_image_tensors[ind]/labels_image_tensors[ind].sum() # 归一化探测器层\n",
    "det_ideal = labels_image_tensors.cpu().numpy().sum(axis = 0)\n",
    "    \n",
    "plt.imshow(det_ideal) # 查看探测器层"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df71fac",
   "metadata": {},
   "source": [
    "### ***D2NN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "edd609c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先做一个模型初始化，将生成的初始随机相位参数与模型分离，以便于后面对其他参数分别训练。\n",
    "# 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "num_layers = 5\n",
    "distance_between_layers = 0.005, 0.004, 0.003, 0.002, 0.001, 0.004\n",
    "phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "                                                          astype('float32'))) for _ in range(num_layers)]\n",
    "Amp = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "                                                          astype('float32'))) for _ in range(num_layers)]\n",
    "# 初始化层间距，预训练时（先只训练相位）设置梯度requires_grad=False。\n",
    "distance = [torch.nn.Parameter(distance_between_layers[i]*torch.tensor([1])) for i in range(num_layers+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "b18f4097",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0.0040], requires_grad=True)"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "6d42025c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DNN(torch.nn.Module):\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    phase & amplitude modulation\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    def __init__(self, phase = [], Amp = [], num_layers = 5, wl = 532e-9, N_pixels = 64, pixel_size = 20e-6, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN, self).__init__()\n",
    "        \n",
    "        # 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "#         self.phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "#                                                           astype('float32')-0.5)) for _ in range(num_layers)]\n",
    "        # 向网络中添加每层相位板的参数\n",
    "        for i in range(num_layers):\n",
    "            self.register_parameter(\"phase\" + \"_\" + str(i), phase[i])\n",
    "        # 向网络中添加每层相位板的衰减参数\n",
    "        for i in range(num_layers):\n",
    "            self.register_parameter(\"Amp\" + \"_\" + str(i), Amp[i])\n",
    "        # 向网络中添加层间距的参数\n",
    "        for i in range(num_layers+1):\n",
    "            self.register_parameter(\"distance\" + \"_\" + str(i), distance[i])\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            constr_phase = 2*np.pi*phase[index]\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*np.pi*torch.sigmoid(phase[index])\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase * torch.sigmoid(Amp[index])\n",
    "        E = self.last_diffractive_layer(E)\n",
    "        Int = torch.abs(E)**2\n",
    "#         output = self.sofmax(detector_region(Int))\n",
    "        output = detector_region(Int)\n",
    "        return output, Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "b7d6ff21",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN(\n",
      "  (diffractive_layers): ModuleList(\n",
      "    (0): Diffractive_Layer()\n",
      "    (1): Diffractive_Layer()\n",
      "    (2): Diffractive_Layer()\n",
      "    (3): Diffractive_Layer()\n",
      "    (4): Diffractive_Layer()\n",
      "  )\n",
      "  (last_diffractive_layer): Propagation_Layer()\n",
      "  (sofmax): Softmax(dim=-1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = DNN(phase = phase, Amp = Amp, distance = distance).to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "b2a44609",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase_0\n",
      "phase_1\n",
      "phase_2\n",
      "phase_3\n",
      "phase_4\n",
      "Amp_0\n",
      "Amp_1\n",
      "Amp_2\n",
      "Amp_3\n",
      "Amp_4\n",
      "distance_0\n",
      "distance_1\n",
      "distance_2\n",
      "distance_3\n",
      "distance_4\n",
      "distance_5\n"
     ]
    }
   ],
   "source": [
    "# 展示可供训练的参数（因为）\n",
    "for index, item in model.named_parameters():\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "231af38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n"
     ]
    }
   ],
   "source": [
    "# 先只训练相位，层间距先设定0.005。之后再在0.005这个基础上微调训练层间距。\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if name.find('phase') != -1:\n",
    "        exec('model.' + name + '.requires_grad_(True)')\n",
    "for name, param in model.named_parameters():\n",
    "    if name.find('Amp') != -1:\n",
    "        exec('model.' + name + '.requires_grad_(True)')\n",
    "    \n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abed7f3b",
   "metadata": {},
   "source": [
    "### ***Training***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "4551157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义训练函数\n",
    "def train(model, loss_function, optimizer, trainloader, testloader, epochs = 10, device = 'cpu', filename = 'best.pt'):\n",
    "    train_loss_hist = []\n",
    "    test_loss_hist = []\n",
    "    train_acc_hist = []\n",
    "    test_acc_hist = []\n",
    "    best_acc = 0\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    for epoch in range(epochs):\n",
    "        ep_loss = 0\n",
    "        # 每个epoch开始时启动Batch_Normalization和Dropout。BN层能够用到每一批数据的均值和方差，Dropout随机取一部分网络连接来训练更新参数。\n",
    "        model.train()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        # 加载进度条\n",
    "        for images, labels in tqdm(trainloader):\n",
    "            \n",
    "            images = images.to(device).squeeze()\n",
    "#             images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "            labels = labels.to(device)\n",
    "            det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "#             det_labels = labels_image_tensors[labels] # 定义各标签的探测器层张量\n",
    "            \n",
    "            optimizer.zero_grad() # 梯度清零\n",
    "            out_label, out_img = model(images) # 得到预测各个探测器上的光强分布以及探测层光强分布\n",
    "            \n",
    "            _, predicted = torch.max(out_label, 1) # 找到光强分布占比最大的探测器，并作为预测结果\n",
    "            correct += (predicted == labels).sum().item() # 得到一个batch的预测结果。如果预测值等于标签值，则正确值加一。\n",
    "            total += labels.size(0) # 得到一个batch的标签总数\n",
    "\n",
    "            full_int_img = out_img.sum(axis=(1,2))\n",
    "#             loss = loss_function(out_img/full_int_img[:,None,None], det_labels) # 光强分布归一化后送入损失函数（与完美探测结果进行比较）\n",
    "#             loss = loss_function(out_label, det_labels)\n",
    "            loss = loss_function(out_label, det_labels*0.1)\n",
    "            \n",
    "            loss.backward() # 反向传播\n",
    "            optimizer.step() # 参数更新\n",
    "            ep_loss += loss.item() # 更新本次epoch的损失\n",
    "        train_loss_hist.append(ep_loss /len(trainloader)) # 计算平均损失\n",
    "        train_acc_hist.append(correct /total) # 计算准确率\n",
    "        #train_acc_hist.append(validate(model, trainloader,device))\n",
    "\n",
    "        #test_acc_hist.append(validate(model, testloader,device))\n",
    "        # if test_acc_hist[-1][0]>best_acc:\n",
    "        #     best_model=copy.deepcopy(model)\n",
    "        \n",
    "        ep_loss = 0\n",
    "        # 不启用Batch Normalization和Dropout。测试过程中要保证BN层的均值和方差不变，且利用到了所有网络连接，即不进行随机舍弃神经元。\n",
    "        model.eval()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        with torch.no_grad(): # 停止梯度更新\n",
    "            for images, labels in tqdm(testloader):\n",
    "                images = images.to(device).squeeze()\n",
    "#                 images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "                labels = labels.to(device)\n",
    "                det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "#                 det_labels = labels_image_tensors[labels]\n",
    "\n",
    "                out_label, out_img = model(images)\n",
    "                _, predicted = torch.max(out_label, 1)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "                total += labels.size(0)\n",
    "\n",
    "                full_int_img = out_img.sum(axis=(1,2))\n",
    "#                 loss = loss_function(out_img/full_int_img[:,None,None], det_labels)\n",
    "#                 loss = loss_function(out_label, det_labels)\n",
    "                loss = loss_function(out_label, det_labels*0.1)\n",
    "                \n",
    "                #直接计算损失，无反向传播和梯度更新。\n",
    "                ep_loss += loss.item()\n",
    "        test_loss_hist.append(ep_loss / len(testloader))\n",
    "        test_acc_hist.append(correct / total)\n",
    "        # 如果最后一次训练的准确率大于之前最好的准确率，则将最后一次的模型保存为最佳模型。\n",
    "        if test_acc_hist[-1]>best_acc:\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            state = {\n",
    "              'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重\n",
    "              'best_acc': best_acc,\n",
    "              'optimizer' : optimizer.state_dict(),\n",
    "            }\n",
    "            torch.save(state, filename)\n",
    "\n",
    "        print(f\"Epoch={epoch} train loss={train_loss_hist[epoch]:.4}, test loss={test_loss_hist[epoch]:.4}\")\n",
    "        print(f\"train acc={train_acc_hist[epoch]:.4}, test acc={test_acc_hist[epoch]:.4}\")\n",
    "        print(\"-----------------------\")\n",
    "        \n",
    "    # 训练完后用最好的一次当做模型最终的结果,等着一会测试\n",
    "    model.load_state_dict(best_model_wts)    \n",
    "    return train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "5f71b821",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_loss(imgs, det_labels):\n",
    "    full_int = imgs.sum(dim=(1,2))\n",
    "    loss = 1 - (imgs*det_labels).sum(dim=(1,2))/full_int\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "47e0c342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义衍射模型基本参数\n",
    "wl = 532e-9\n",
    "pixel_size = 10*wl\n",
    "# 定义模型，损失函数和优化器\n",
    "model = DNN(phase = phase, Amp = Amp, num_layers = 5, wl = wl, pixel_size = pixel_size, distance = distance).to(device)\n",
    "# criterion = custom_loss\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=0.002)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "9ce007f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.30it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 24.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=16.73, test loss=16.56\n",
      "train acc=0.9009, test acc=0.9514\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.57it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 24.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=16.53, test loss=16.5\n",
      "train acc=0.9544, test acc=0.9584\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.33it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 24.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=16.5, test loss=16.49\n",
      "train acc=0.9615, test acc=0.961\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 正式开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 3,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1b62795d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('phase_0', Parameter containing:\n",
      "tensor([[ 0.9268,  0.3355,  0.6106,  ...,  0.5415,  0.0695,  0.5406],\n",
      "        [ 0.2576,  0.3050,  1.0779,  ..., -0.0386,  0.7660,  0.7550],\n",
      "        [ 1.0169,  0.8694,  0.4366,  ...,  0.2982,  0.4471,  0.0098],\n",
      "        ...,\n",
      "        [ 0.3694,  0.8991,  0.4327,  ...,  0.3350,  0.8323,  0.1003],\n",
      "        [ 0.3170,  0.0562,  0.4824,  ...,  0.8881,  0.3954,  0.5316],\n",
      "        [ 0.7993,  0.7127,  0.6952,  ...,  0.1952,  0.7930,  0.2592]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('phase_1', Parameter containing:\n",
      "tensor([[ 0.0969,  0.5901,  0.3346,  ...,  0.0876,  1.0342,  1.1947],\n",
      "        [-0.1244,  0.6115,  0.4483,  ..., -0.0392, -0.1951,  0.2686],\n",
      "        [ 0.7266,  0.6955,  0.7756,  ...,  0.2041,  0.9050,  0.0873],\n",
      "        ...,\n",
      "        [ 0.9383,  0.8794,  0.4841,  ...,  0.6015,  0.5290, -0.0050],\n",
      "        [ 0.0535,  0.5821,  0.0431,  ...,  0.8666,  0.8463,  0.4174],\n",
      "        [ 0.1519,  0.1589,  0.1361,  ...,  0.9141,  0.0313,  0.6221]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('phase_2', Parameter containing:\n",
      "tensor([[1.6422e-01, 4.8410e-01, 8.8441e-01,  ..., 8.2031e-01, 8.2269e-01,\n",
      "         2.8040e-01],\n",
      "        [3.7707e-01, 3.1185e-01, 9.6079e-01,  ..., 5.8218e-01, 2.9807e-01,\n",
      "         3.0331e-01],\n",
      "        [7.2215e-01, 9.9353e-01, 9.4265e-04,  ..., 5.1481e-01, 9.5184e-01,\n",
      "         5.0228e-01],\n",
      "        ...,\n",
      "        [3.4027e-01, 7.8474e-01, 3.9266e-01,  ..., 5.2234e-01, 3.6514e-01,\n",
      "         9.5347e-02],\n",
      "        [1.0403e+00, 5.2567e-01, 7.6074e-01,  ..., 1.6049e-01, 1.1383e+00,\n",
      "         6.0740e-01],\n",
      "        [7.2850e-01, 2.5862e-01, 1.2339e-01,  ..., 6.3577e-01, 9.9967e-01,\n",
      "         2.3563e-02]], device='cuda:0', requires_grad=True))\n",
      "('phase_3', Parameter containing:\n",
      "tensor([[ 0.4782,  0.9060,  0.1036,  ...,  1.0312,  0.1888,  0.1966],\n",
      "        [ 0.9135,  0.7526,  0.2600,  ...,  0.5543,  0.4620,  0.0581],\n",
      "        [ 0.5559,  0.0022,  0.3456,  ...,  1.1147,  0.3496, -0.0257],\n",
      "        ...,\n",
      "        [ 0.4181,  0.9035,  0.2541,  ...,  0.2917,  0.7565,  0.2363],\n",
      "        [ 0.4689,  0.7674,  0.6930,  ...,  0.1620,  0.6087,  0.3470],\n",
      "        [ 0.3802,  0.6699,  0.1877,  ...,  0.8745,  0.7035,  0.0779]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('phase_4', Parameter containing:\n",
      "tensor([[ 0.4150,  0.5162,  0.5145,  ...,  0.1530,  0.7641,  0.6116],\n",
      "        [-0.0132,  0.2154,  0.5271,  ...,  0.4144,  0.4435,  0.8558],\n",
      "        [ 0.9547,  0.3807,  0.7480,  ...,  0.1492, -0.0951,  0.4202],\n",
      "        ...,\n",
      "        [ 0.2628,  0.3470,  0.6545,  ...,  0.6352,  0.4004,  0.5158],\n",
      "        [ 0.3839,  0.3660,  0.1057,  ...,  0.8496,  0.9240,  0.2601],\n",
      "        [ 0.5178,  0.8035,  0.1423,  ...,  0.8260,  0.8258,  0.8958]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('Amp_0', Parameter containing:\n",
      "tensor([[ 0.2434,  0.1212,  0.1627,  ...,  0.2690,  0.0476,  0.0872],\n",
      "        [ 0.0076,  0.0528,  0.2297,  ...,  0.4206,  0.2437,  0.1464],\n",
      "        [ 0.4092, -0.0630,  0.1643,  ...,  0.0788,  0.0307,  0.1140],\n",
      "        ...,\n",
      "        [ 0.4223,  0.1107,  0.2651,  ...,  0.1524,  0.0769,  0.1346],\n",
      "        [ 0.0531,  0.0243,  0.2415,  ...,  0.1943,  0.1047,  0.0676],\n",
      "        [-0.0029, -0.0178,  0.1397,  ...,  0.0655,  0.2456,  0.1914]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('Amp_1', Parameter containing:\n",
      "tensor([[ 9.0163e-01,  2.0050e-01,  5.7144e-01,  ...,  7.1108e-01,\n",
      "         -1.1916e-01,  3.1961e-01],\n",
      "        [ 5.2828e-01,  1.7262e-01,  1.3046e-01,  ...,  2.3166e-01,\n",
      "          5.3599e-01,  4.9944e-01],\n",
      "        [ 1.9375e-01,  2.4664e-01,  4.8827e-01,  ...,  1.0435e+00,\n",
      "          5.3282e-01,  6.1022e-01],\n",
      "        ...,\n",
      "        [ 4.6310e-01,  4.9382e-01,  6.3094e-01,  ...,  8.6215e-01,\n",
      "          7.7298e-01,  5.9333e-01],\n",
      "        [-1.5803e-01,  2.1278e-01,  3.4912e-01,  ...,  2.3059e-01,\n",
      "          2.6278e-01,  4.3031e-01],\n",
      "        [ 2.7778e-01,  1.0253e+00, -1.8384e-04,  ...,  8.3301e-01,\n",
      "          3.8683e-01,  4.4621e-01]], device='cuda:0', requires_grad=True))\n",
      "('Amp_2', Parameter containing:\n",
      "tensor([[1.1330, 0.7791, 0.3593,  ..., 0.3570, 0.3332, 0.5461],\n",
      "        [0.7796, 0.1095, 0.4590,  ..., 0.3993, 0.7170, 0.7096],\n",
      "        [0.3188, 0.1551, 0.0809,  ..., 0.2501, 0.2270, 0.5577],\n",
      "        ...,\n",
      "        [0.9849, 0.5576, 0.9287,  ..., 0.1757, 1.2165, 0.3625],\n",
      "        [0.3999, 0.7303, 0.2987,  ..., 0.9610, 0.2516, 0.7065],\n",
      "        [0.6619, 1.0892, 0.4262,  ..., 0.3397, 0.1148, 0.1545]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('Amp_3', Parameter containing:\n",
      "tensor([[ 1.2211,  0.8239,  0.8943,  ...,  0.6782,  0.5757,  0.8468],\n",
      "        [ 0.6876,  0.9592,  0.2687,  ...,  0.4249,  0.5940,  0.8177],\n",
      "        [ 0.7856,  0.4502,  0.5673,  ...,  0.5769,  0.7531,  0.4122],\n",
      "        ...,\n",
      "        [ 0.5903,  0.3648,  0.5087,  ...,  0.3997,  0.3109,  0.3360],\n",
      "        [ 0.5368,  0.1075,  0.3079,  ..., -0.1761,  0.9170,  0.4299],\n",
      "        [ 0.6077,  0.5831,  0.3411,  ...,  0.7382,  0.3727,  0.2969]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('Amp_4', Parameter containing:\n",
      "tensor([[ 0.4647,  0.4296,  0.6396,  ...,  0.1511,  0.2643,  0.1021],\n",
      "        [ 0.2652,  0.6236,  0.3595,  ...,  0.4651,  0.3167,  0.5901],\n",
      "        [-0.0437,  1.1579,  0.3409,  ...,  0.4421,  0.5043,  0.3671],\n",
      "        ...,\n",
      "        [ 0.1595,  0.4170,  0.1987,  ...,  0.3740,  0.6796,  1.0411],\n",
      "        [ 0.2338,  0.5683,  0.4562,  ...,  0.4564,  0.6945,  0.4461],\n",
      "        [ 0.8462,  0.7495,  0.2662,  ...,  0.3449,  0.2126,  0.0896]],\n",
      "       device='cuda:0', requires_grad=True))\n",
      "('distance_0', Parameter containing:\n",
      "tensor([0.0050], device='cuda:0'))\n",
      "('distance_1', Parameter containing:\n",
      "tensor([0.0040], device='cuda:0'))\n",
      "('distance_2', Parameter containing:\n",
      "tensor([0.0030], device='cuda:0'))\n",
      "('distance_3', Parameter containing:\n",
      "tensor([0.0020], device='cuda:0'))\n",
      "('distance_4', Parameter containing:\n",
      "tensor([0.0010], device='cuda:0'))\n",
      "('distance_5', Parameter containing:\n",
      "tensor([0.0040], device='cuda:0'))\n"
     ]
    }
   ],
   "source": [
    "# 查看训练的各个参数，确保这一次只有相位被训练了（requires_grad=True）\n",
    "for param in model.named_parameters():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "5a17d46d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n",
      "\t distance_0\n",
      "\t distance_1\n",
      "\t distance_2\n",
      "\t distance_3\n",
      "\t distance_4\n",
      "\t distance_5\n"
     ]
    }
   ],
   "source": [
    "# 全部解禁，相位和层间距都拿来训练\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = True\n",
    "\n",
    "# for name, param in model.named_parameters():\n",
    "#     if name.find('distance') != -1:\n",
    "#         exec('model.' + name + '.requires_grad_(True)')\n",
    "\n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ca35e2ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载之前训练好的参数\n",
    "checkpoint = torch.load('best.pt')\n",
    "best_acc = checkpoint['best_acc']\n",
    "model.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "717642ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载损失函数和优化器\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "98f7eb5b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.05it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=4.159, test loss=4.086\n",
      "train acc=0.8891, test acc=0.8965\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.34it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=4.149, test loss=4.082\n",
      "train acc=0.8889, test acc=0.8965\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.48it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=4.146, test loss=4.08\n",
      "train acc=0.8884, test acc=0.8963\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.41it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=3 train loss=4.144, test loss=4.076\n",
      "train acc=0.8885, test acc=0.8961\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 重新开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 4,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "344e84fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[ 0.6431,  0.4833,  0.8031,  ...,  0.5384,  0.7097,  0.2208],\n",
      "        [-0.0837,  0.1288,  1.2072,  ...,  0.4694,  0.4411,  0.9489],\n",
      "        [ 0.8853,  0.5419,  0.6499,  ...,  0.2383,  0.2275,  0.2814],\n",
      "        ...,\n",
      "        [ 0.6345,  0.5225,  0.4692,  ...,  0.8675,  0.3404,  0.0501],\n",
      "        [ 0.3948,  0.1209,  0.7719,  ...,  1.0150,  0.1261,  0.2964],\n",
      "        [ 0.5607,  0.6085,  0.7333,  ...,  0.4668,  1.0453, -0.1000]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.9174,  0.2537,  0.7535,  ...,  0.4043,  0.4600,  0.0922],\n",
      "        [-0.0217,  0.4729,  1.1809,  ...,  0.8328, -0.3390, -0.1193],\n",
      "        [ 0.8926,  1.2378,  1.1023,  ...,  1.0252,  0.0594,  0.3596],\n",
      "        ...,\n",
      "        [ 0.0435,  0.6821,  0.7935,  ...,  0.6290,  0.2357, -0.1543],\n",
      "        [ 0.1809,  0.2682,  0.1268,  ...,  0.7229,  0.1058,  0.0792],\n",
      "        [ 0.9208,  0.6339,  0.2333,  ...,  0.1896,  1.0296,  0.5069]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.3164,  0.5183,  0.2457,  ...,  0.2376,  0.5020,  0.6713],\n",
      "        [-0.2158,  0.5712,  0.1402,  ...,  0.0879,  0.8264,  0.1541],\n",
      "        [ 0.4388,  1.3248,  0.3967,  ...,  0.2752,  0.3514,  0.5308],\n",
      "        ...,\n",
      "        [ 0.4901,  1.1249,  0.8612,  ...,  0.3630,  0.5576, -0.2496],\n",
      "        [ 0.2512,  0.2907,  0.0783,  ...,  0.2522,  0.9674,  0.4417],\n",
      "        [ 0.6307,  0.0742,  0.7116,  ...,  0.6782,  0.4837,  0.4804]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.3478,  0.3672,  0.9258,  ...,  0.4782,  0.3088,  0.9675],\n",
      "        [ 0.7564,  0.5824,  0.2450,  ...,  0.4874,  0.5146,  0.2156],\n",
      "        [ 0.5603,  0.0432,  0.4944,  ...,  0.3371,  0.0835,  1.1271],\n",
      "        ...,\n",
      "        [ 0.2245, -0.0993,  0.6130,  ...,  0.6255,  0.4557,  0.1883],\n",
      "        [ 0.4851, -0.0064,  0.5330,  ...,  0.7285,  1.3432,  0.0586],\n",
      "        [ 0.6834,  0.5148,  0.3176,  ...,  0.4206,  0.8934,  0.8021]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-0.0734,  0.6901,  0.0853,  ...,  0.9803,  0.5891,  0.3223],\n",
      "        [ 0.0438,  0.8067,  0.1972,  ...,  0.1060,  0.7213,  0.4646],\n",
      "        [ 0.1363, -0.1322,  0.2482,  ...,  1.1555,  0.7594,  0.5206],\n",
      "        ...,\n",
      "        [ 1.0744,  0.8666,  0.2687,  ...,  0.1973,  0.7954,  0.6045],\n",
      "        [ 0.5061,  0.2781,  0.6476,  ...,  0.5418,  0.1829, -0.0687],\n",
      "        [ 0.7670,  0.5251,  0.9132,  ...,  0.7898,  0.4059,  0.1664]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.2397,  0.2040, -0.0680,  ..., -0.2366, -0.2330,  0.1441],\n",
      "        [-0.3205,  0.0467,  0.0292,  ...,  0.0838,  0.0484,  0.0650],\n",
      "        [-0.1057,  0.0463,  0.0490,  ...,  0.1833, -0.0475,  0.3281],\n",
      "        ...,\n",
      "        [-0.2450,  0.1929,  0.0179,  ...,  0.1982, -0.0524,  0.3042],\n",
      "        [ 0.0282,  0.0903,  0.1151,  ...,  0.1017,  0.0114,  0.1769],\n",
      "        [ 0.2788,  0.1910,  0.1969,  ...,  0.0038,  0.2199, -0.0688]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.5133,  0.8272,  0.8771,  ..., -0.0184,  0.4146,  0.0899],\n",
      "        [ 0.3405,  0.8541,  0.9401,  ...,  0.5909, -0.2351,  0.3813],\n",
      "        [ 1.2024,  0.2964,  0.3884,  ...,  1.0197,  0.2720,  0.4517],\n",
      "        ...,\n",
      "        [ 0.3034,  0.5236,  0.5672,  ...,  0.0351,  0.3290,  0.4588],\n",
      "        [ 0.5959,  0.7470,  0.1823,  ...,  0.7871,  0.6696,  0.4727],\n",
      "        [ 0.8611, -0.0591,  0.5697,  ...,  0.5923,  0.7176,  0.7674]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 2.6491,  1.0511,  0.5080,  ...,  1.5479,  0.4574,  1.2015],\n",
      "        [ 1.7072,  0.9216,  1.2585,  ...,  0.7319,  0.4455,  0.6094],\n",
      "        [-0.3048,  0.8667,  1.4490,  ..., -0.7719,  1.9344,  1.3831],\n",
      "        ...,\n",
      "        [ 1.4148,  0.3708,  0.7644,  ...,  0.5743,  0.8847,  0.4587],\n",
      "        [ 1.2651,  1.1852,  0.7301,  ...,  0.3873,  0.9204,  0.9605],\n",
      "        [ 0.9809,  0.6845,  0.3419,  ...,  0.2917,  0.4360,  0.8760]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.0455,  0.2959,  0.2810,  ...,  0.0420,  0.1021, -0.1204],\n",
      "        [ 0.1201,  0.0698,  0.0501,  ...,  0.0562, -0.1003,  0.0613],\n",
      "        [ 0.1204,  0.1509,  0.1473,  ...,  0.0660,  0.2486,  0.1443],\n",
      "        ...,\n",
      "        [-0.1113,  0.0439,  0.0833,  ..., -0.0080,  0.1239,  0.0414],\n",
      "        [ 0.1201,  0.0136, -0.0670,  ...,  0.0304,  0.0929,  0.0320],\n",
      "        [-0.0280,  0.1078, -0.0874,  ...,  0.0112,  0.0470,  0.0975]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[2.0971, 2.8210, 2.1239,  ..., 2.1877, 2.6128, 2.9002],\n",
      "        [2.2892, 2.6438, 2.0412,  ..., 1.9927, 2.4301, 2.4863],\n",
      "        [1.6876, 1.9055, 1.4035,  ..., 1.6421, 1.5435, 2.0050],\n",
      "        ...,\n",
      "        [1.2733, 1.8362, 1.4886,  ..., 1.5526, 1.8605, 2.0709],\n",
      "        [2.4173, 2.8737, 2.5427,  ..., 1.9643, 2.4785, 2.3724],\n",
      "        [2.3284, 2.7446, 2.4193,  ..., 2.4151, 2.6104, 2.8567]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0050], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0030], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0020], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0010], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0', requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa9faa1",
   "metadata": {},
   "source": [
    "### ***Saving***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ec77081",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'MNIST_0.7491.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "239983a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 释放显存\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619ac490",
   "metadata": {},
   "source": [
    "### ***Loading***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "771976ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('MNIST_0.7426.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d61eb80",
   "metadata": {},
   "source": [
    "### ***Data Analysis***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "1e175e1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00500000\n",
      "0.00400000\n",
      "0.00300000\n",
      "0.00200000\n",
      "0.00100000\n",
      "0.00400000\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if name.find('distance') != -1:\n",
    "        print('{:.8f}'.format(param.cpu().detach().numpy()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "adac227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "ff703d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 3)\n",
    "def visualize(image, label):\n",
    "    image_E = torch.sqrt(image)\n",
    "    out = model(image_E.to(device))\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)\n",
    "    ax1.imshow(image.squeeze(), interpolation='none')\n",
    "    ax1.set_title(f'Input image with\\n total intensity {image.squeeze().sum():.2f}')\n",
    "    output_image = torch.abs(out[1].squeeze()).detach().cpu()\n",
    "    ax2.imshow(output_image*det_ideal, interpolation='none')\n",
    "    ax2.set_title(f'Output image with\\n total intensity {output_image.squeeze().sum():.2f}')\n",
    "    dist = out[0].squeeze().detach().cpu()\n",
    "    ax3.bar(range(len(dist)), dist)\n",
    "    fig.suptitle(\"label={}\".format(label))\n",
    "    plt.show()\n",
    "\n",
    "def mask_visualiztion():\n",
    "    for name, mask in model.named_parameters():\n",
    "        if name.find('phase') != -1:\n",
    "            plt.imshow(torch.sigmoid(mask.detach().cpu())*360, interpolation = 'none')\n",
    "            plt.title(f'Mask of layer {name}')\n",
    "            plt.colorbar()\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "38979bf8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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048YN8vT0fO9nNRoNPXv2jKytrfUOYxYIBP8dkiRRUlISubm5GWRiqvT0dMrMzMzz8qamppCeId/wXzh/V6tWTerbty+8V6pUKWn06NG5fjYqKuq9wQPiJV7i9XFfUVFR/8W08a9IS0uTXJyM9doOFxcXKS0t7WMPXW8++JV2ZmYmRURE0OjRo+H9Ro0a0alTp3Isn5GRAQ9fpL8l9gZbepDSypSIiB4c5GQ6aYXV8Pm+dTgN6crdDcFmXDIJ+rbbCvA4bfAqPq60JLfNC+sEaFyyga6mAq/X1jIdbFl7MVmTU/vHcjs2pQDYklM4HLyC+zOwXTtYAvqe++Pl9r2BGEbeyTdCbm/6szrYutdCWep1lpXcPryjKti0923xkphfI3OOC/QTvE3ktuVzDdgkLa+0V2XQRU0yk6BvnMLHYWDHXWBbMwvD4NMceFm1TiR96cZ35fZot/1gG37/K+g/P+0mt811EiJqJyue1G8N2Ibuw2ReXn/wVZ33pNtga2x3jcdz8GuwmbqmQL/wUv4ZvhqE51PCM3z4aBbLy3qG4fn9ujSfXymF8fzu1OYY9O+mOsntvk5o25fEyacOLK0pt9WZ6XR902SDfCCamZlJMbFqiozwIhvr3O8CEpM05F35EWVmZua7q+0PPmm/fPmS1Go1OTs7w/vOzs5yxjVtQkNDaeLEiTkHZmVKJn9P2sZmvFONLHDSNi/Am2Cks/ONLfFWSWnCdrUpntRGFjyZGFtm4WDMcL0KLbuxFU5CGlNcVmnFs4uxhDONkcTLvtlWeVmd71Qa82eNLNFmVoAnUCMLtJlr2YiIzLK4r/sd2vtWe9xERBqlzr415fUoTd49aRub46St0Z20VXwcLArg6Wissy+NzbSOmc6krb3/Cuj8aHW3xVjrPDHG3Q5jt7LGseueX0olf49pAVyRpdZndY+JsSWew0qlUsuG+yfHZ814WaUxnqfa+wv2FeU8D0wU795fZpLWOWKac0IzZNnSqkD2KzfUUu7LGCr/mTCle2AlnTSibxgzZgwlJCTIL1HjTiAQ/FM0JOX5lV/54Ffajo6OZGxsnOOqOjY2NsfVN1F2DuW3ZY27c6qIfGVTrMlD+f0SNrGw3L1UXmdY9xlga/8TZsor2J/XkzAfH4hKVnj1o02xJlh5JnIvJ5sv9xXazps5Qf/hkSJye1r3NWBb0KuT3L5WsxTYHG7jeF5+YSe3/YreAtuOSL6dVSbh//CmB19APzWFr5zC+k0HW719Q3ncJ7zA5vvTHejP9eAiuF0u9saxr2YJ5oevUKpI1eAV6cpTdeT2rPXtwGaiEwnv+xVXi3k2pTjYLtqznPSdL1YjN1mGK3JLZDluwv9Wgq3/8v5ye8a3uJ4JK7ZAv0gbLlzQa1tfsLVtwyl4ewQcB9v9VJTQnljw2JNTdK7m7fBu0dSNJZHbnlZgU6TzHY9VYZROVu/DCkB2ZVkXan+jP9hMY/hKO7MKn4eaNDXRL2TQaEhDmtwXy+NShskHv9I2NTWlypUrU1hYGLwfFhZGNWrU+NBfJxAIBDJqScrzK7/yn7j8DR06lLp160ZVqlSh6tWr0/Lly+nx48fUt2/f3D8sEAgE/5C8Sh/5WR75zyIiFy9eTNOnT6fo6Gjy9fWlOXPmUJ06dXL9XGJiItna2pLHsvHyQ5jCO/h2TaHB4Q6duUFuTwvG29mB43+H/sVkvuXfeasC2H6svFduTzqDXgvWdqnQdyzAT/97uKNHzKbWAdC/362Q3C54E8eeYcc3OoU7RIJN8x3eJj9twfUSTZJwPQ7XeTzGcTjW6On4v2w/n2+p+y/B/ZOk4e8M2YYeF2Zx+Dwi3YHHoHbF0OviS/mW+l5nC7CZxuPNXZa11i190QSwpaaibOa5hrcl0QMfrKU0YY8fFzssrNvHE+WJSevZm8M4DUyk1trtXzS7Abbr68tAv2L3q3L70fiSYMuw5QeRljEocSS743b1mbBNbk88jeeeiQU+bFTHWMrtgQ1RejoTz7JdeWv0/nmWYQf9iJdahRzWFQJby9FH5faKP/3ltiYtnZ4MGU8JCQlkY4MeVR+bN/PG/VsuZJ0H75GkJA0VKxVjkNuSG/9Z7pH+/ftT//79c19QIBAIPhB5lT6EPCIQCAQGgObvV16Wy6+ISVsgEHwyqEkidR706rwsY6gY7KTt5fpKDooYO5P15qvpHrDctTTW5l5ggB9NvNQc+m1LXpHbUizqihOPt5bbprG4W8qVuA/9J8l2cnvCibZga7/xAvRVA9nTPyoI3fiWVeaq5j3C0W2ucEUcQ8FmHDFZ1xnd724mc7Ti/Th0J0u9VBD66gqsTT/KxGVX3eFoSmUaatiKmnG4niesA1reRP39+She1viWJdh29US3zEHtfpDbxZfquE8uQHdF41QWoNMd0XUwPYGP56NYjN7cZVkR+g7X+Dj4T8BnEp5m7Aq3eHEbsCX6YbSip8VruX2sHeqo9he5/6KdznXdI9y3i37Wen5QDc8R2xO4nZm2/NmF+5qAbUrL3+T2uL2dwKZ7PK21dnVyG4wAXnWwLnfsVNxWGf71qVrKW+BMfg6uMdhJWyAQCPRFyCMCgUCQj9CQgtSUe5i9Jg/LGCoGVwThjetO2e9D5LwHGfXYhSvtNbqQFV/Ht2/PaqGt4F281UzoylFiFqboSuVpw7f0ETe8wba6AUbNDZnD/uaKRphxSBWO0XfJnvyfvqLFClzv89pye5zbH2Ab1G0A9I2yeFsW/7YYbA2ODJbbJb0wErWACbrj3d3G0XeOV9AWXYMlhsIBmE7g2SGUpVzO8WdflkepyfU4u+49+wmvacoUeg59jcQ/notnfMBWu9Z16J84WVZuK9xQqnDawRLNhBA8Xmue14L+6escTVlsA54jz2rzejJKoj9gscW4LU/qs/tkuiPaHIqxdGI9H13K4ouiu6LNIz6HJy3Cc2RC/++hP2fJQrndbvcgsElmPIYxdfaC7XwintOPgngf3P0Br92sC7LbaAsvPgYZyVk0t9Zug3STezNvXLjunCOXyttITtJQlbLPDXJbckNcaQsEgk8GdR6vtPOyjKEiJm2BQPDJICZtgUAgyEdkSUaUJeUuj2QZlCisHwY7aSd7SWRknr1nbc04DHha3W2w3GDjLnLbc7sKbBZPMdn8zArsYncnEzMOGiv4KMbMLQa2HVXQ9SytDmvj33pfBNtfDu7Q97BkrXziMHTrS/qWtfoOMahdpvTEXM6/1Vsut5tsxOyFNetw9rtTd3DsG+osh37PgqwbJ3jruM05a4Wf33YFm6Ik6t/p9/jUWTVoLtoGsm3YrY5gu7Mew73TtQobnOqD7oC3sjCLXVxVdh98sQg12gojLsvtwPNdwOa2XifXdQXWlMevQg15+MR+cnt2jw1gGx/WC9cTzeeM41XUtIv5RcvtU71183CjVj6/MmvwbU/0A5uxP/5Ev149RG5bYyI/KnSZn9OsOdIKbDHNMZTergI/hyjqgSHvxj+xm2iBZXzcdfN3GyJqMiJ1HvLgvTunp+FjsJO2QCAQ6IskKeDh9vuWy68YXnVOgUAg+Ie80bTz8soroaGhVLVqVbK2tiYnJydq06YN3b59O8dyN2/epFatWpGtrS1ZW1vTl19+SY8fc7nBjIwMGjhwIDk6OpKVlRW1atWKnjx5ovc2iklbIBB8Mqglozy/8kp4eDgNGDCAzpw5Q2FhYaRSqahRo0aUksLy6/3796lWrVpUqlQpOnbsGP3111/0008/Qf3JoKAg2r59O23atIlOnjxJycnJ1KJFC1Kr9RNrDNZPe9Pl0nKdvbELWUvs8R2mo1y8r7Hc9lkfD7ZbfbAAqddu3tS4UugrOztwmdweM+kHsOkeX4drHPZ7+3vUK02fo+KkKsL+xEVc0ac7Y6lWutVk1ERNR0RD39eOw9jjszA0vKXDZbk98UYLsCUlo++62TXuV251DWyXtvvK7bQKqLtqXunows9Yc6/zFer6rQpektuBO78Fm25qVo8DLMzeHYDHpFkZ9NM+HcOpdW3MUWN/+tJObvcsewZs6zfXh76qLP/YGhTHK6Yr0zhlb0wNvBrz3o6+4UFrNsntxY0wpDyhEj8zWTxrHthGteoJ/eRitnJ75dzZYPs1vhr0j43jQrutpx0CW1hXP7ldcDH66z+eg4Wi03rws5b0U5jOIM2NJxHT13yc1enp9CBkrEH6Nr+ZN/ZeKZqjtufbSElSU/PyD/7Rtrx48YKcnJwoPDxcTjfduXNnMjExoXXr1r31MwkJCVSoUCFat24ddeqUnWLg2bNn5OHhQX/88Qc1btz4rZ97G+JKWyAQfDLoK48kJibCKyMjI5dvyJ6AiYjs7e2JiEij0dDevXupRIkS1LhxY3JyciI/Pz/asWOH/JmIiAjKysqiRo0aye+5ubmRr68vnTqF+W9yQ0zaAoHgk0FfecTDw4NsbW3lV2ho6HvXL0kSDR06lGrVqkW+vtl3prGxsZScnEzTpk2jJk2a0MGDB6lt27bUrl07Cg8PJyKimJgYMjU1pYIFMYGbs7Nzjnq6uWGw3iNjt3wjF/Y10YqSXr8QbyOGDOICs5frYLHeMYX+hH5PK5ZZ3Hbj/1XgRXYTQ1GFqN6g09DfHFFFbrscxluxxCJ4S10shKWUBr+jHFE09IjcHvVbN7Ct8t4F/ZAGXPQ2cnoBsP35mN3fjIxQ7TJWol4maR3xM4+KgM3mNX/WNBxln9p9zkM/fBXfth/bhS6RV6+zxPDlcCxC/DgJT9rHCpaIpHR0Kfva4fQ7+wNnYJi/+112advjWBdsK0IWQj9dYhnmu4Pohln6JssGRio7sMWVRqlp4Vec4fFlAC6b6sLnwTfzh6JtFEpPRRey7NLh0ndgs16Pt+4xX/N2LjrUCGyn98yS2w1nolvo4um4D44mcxWeZy449v1n+PhlePD3adLQbdAQ0ZAiT3lF3iwTFRUF8sjbioxrExgYSFeuXKGTJ0/yujTZ0mbr1q1pyJBsl8yKFSvSqVOnaOnSpeTv7//WdRFl/wkoFPp5sogrbYFA8MmQJSkpMw+vrL+vXmxsbOD1vkl74MCBtGvXLjp69Ci5u3M8hqOjIymVSipTBsvRlS5dWvYecXFxoczMTIqLwxTHsbGx5OyMMSO5ISZtgUDwyaAhozy/8ookSRQYGEjbtm2jI0eOkLc3BnaZmppS1apVc7gB3rlzh7y8sh+eV65cmUxMTCgsLEy2R0dH07Vr16hGjRp6baPByiMCgUCgL2pJQeo8BM7kZZk3DBgwgDZs2EA7d+4ka2trWYO2tbUlC4tsyWzEiBHUqVMnqlOnDtWtW5f2799Pu3fvpmPHjsnL9u7dm4YNG0YODg5kb29Pw4cPp3LlylGDBg302kaDnbTt7kpkbJqtsS6dPFd+/3IGpghdFsnpTb/xOge2bXFVoB/y5Xa5HV8V3eZm7eaw3/5jtoJt8h/toO90iQ94nwm47AsVKuIbq/MYfp3/bree38fOgf7WhMrQTy3BFbONjLDiuufPPJ6BG7eALeReM+hnPuXQ8Kk9NoKtn4qr2btvQve7L6weQn+fC5cJUqbiD0Btyn0fq1iw6fZN2nPF8/8dRS36m0N9oK9dIaqAFX7nlzNYc//zpy/BNmIsFphuNvaY3Da2QR399hjeP8trYIrXH3ZiqgG1CevzKoy4pyXfcfrcERMwND05Ht0nqyxmrf7Ui6Jg6zsVXVzH/MlVbkyTcR/47eEQ942DF4BtxDDcB9V+5P11bAc+k/A5zM9hHjXjDVOnG37wd97D2PPu6bxkyRIiIgoICID3V69eTT179iQiorZt29LSpUspNDSUBg0aRCVLlqStW7dSrVqcFnjOnDmkVCqpY8eOlJaWRvXr16c1a9aQsXHuLoraGOykLRAIBPqikYxIk4fAGY0e4Sl5DWXp1asX9erV6512c3NzWrBgAS1YsOCdy+QFMWkLBIJPhv/iStvQMNhJu9+obWRZIPu2YXZMQ/n9i9Eoj9hbsVRgZYSO8XuOojxyJJrd1DQ6W+7Xmm/TfxmEGdI2L5sP/R9ucaWYdQNagu1xY7z1ndtutdwOjEa3vhIr2dVr7St8GHH9u9LQf9qBB9y12CWwWa7m7X6YWQhsxksx2m309F/l9sh5eLtvpuVdlojekxR8oD30pcIsK3iuxdtm59BIue1thnLI/lfloH/mPj/UqVgJCygXK/AS+jv3s+yRXBJljZ2b+TbU1gqjSxO88Ue8+kiA3G5X5yzYDqzn4saB5pgtsG+jMOhvLM7nF4pJRPsTystttY5Dgtcu3F8FavPxy/ofFiUe2xwLRzcoyxkdD2nwHOlelWWWfrMGgi3DF7q0+w4fB+8wTBeo0PCE9m1bjrpMT86iSVPJoNFQ3vRqUSNSIBAIDIC8eobo4z1iaOg98uPHj1PLli3Jzc2NFAoFhGoSZes/wcHB5ObmRhYWFhQQEEDXr19/+8oEAoHgA/JfJIwyNPQeeUpKClWoUIEWLlz4Vvv06dNp9uzZtHDhQjp//jy5uLhQw4YNKSkp6a3LCwQCwYciSzLO8yu/orc80rRpU2ratOlbbZIk0dy5c2ncuHHUrl22m9zatWvJ2dmZNmzYQH369Hnr597G/8a3IaVJdij1s44cPmt7DEOJE805Q9qau63BFjgT3aWW/cbub1m2qGr9+RdnQWsdilnrxnRF7bfsLL5zuLMQo6B+7zAX+uMf85jm1fsVbdd6yu20Sai/v26Mh6Zmvatye/+0OmDLtGYNz1gn0vhFU6zmM+IMa9NFbuPCj5rxd2bZoi74Xd2j0F95hTX4VCfU8a2UrNGu/7Y52EatXw/9SwdYl7Usjjr1QMcT0D9Zid3hlpTCqjJrXnP2uz8XVgWbWTw+dNJ8wVn+9v1WHWzGdThiTRGBIfeEEjIl3GO7+Qu8/nEtxhXp4wIwO2C54vegf2go6/Ft5qJuvmx/Q+i/KMIpDPp/icdk76h6cjulFm5zlgOeB0uqcobCoQ0xlF/7HPp9LvsQqzPTiQgzCxoaeX8Q+Rldab+PyMhIiomJgUxWZmZm5O/v/85MVhkZGTkybQkEAsE/QfN35Zq8vPIrH3TSfhMppBtL/75MVqGhoZBly8PD463LCQQCQW5o/r7Szu31WT2IzAu6Wavel8lqzJgxlJCQIL+ioqL+iyEJBILPgDfBNXl55Vc+qMufi0u2j2lMTAy5unLKzfdlsjIzM3trZq0vRl4iswLZ3q8OJqxB/mqG2q/5AXYurjr1AthW/w9DuLPcWccufAw17djKvCt2nsOw3poz70D/58J/yO3JQagL97uFvr2WJqzTnkkuDjbnNlw/LmEVVnFX6DiSWmhVwk5zxBPOoTXXmYs9iOsxscHxeS7jBzAvKqEWLRmz/7DKDn2JE9T4LEHxnI9ZfHEcz63J7AMc8utSsEVlOUC/XCNOshOdimlIe/YaDP0xSzjsvvtfPcGm0tKfJ49B3Tx0RlfoW+9hXThN57RMTuKUtM538CBsmY6pUIv34AuMO4/Qv3rZL6zlV22F6WnvTcfnIE+1wgI2LsLvMA5Igf6jzcXk9vMWmKL3mxn8DMfBOBls4/7oBP2hq1nHNtXxETCLYz08viS/r0Fp3iDJa/1HfWpEGhof9O/G29ubXFxcIJNVZmYmhYeH653JSiAQCPRFXGm/heTkZLp3j59+R0ZG0uXLl8ne3p48PT0pKCiIQkJCyMfHh3x8fCgkJIQsLS2pS5cu71mrQCAQ/HvUlLeraMNPffVu9J60L1y4QHXrcja2oUOzq3L06NGD1qxZQyNHjqS0tDTq378/xcXFkZ+fHx08eJCsrXXrwbyfW0NKkdI4+1ZVPS1efr9FUQzUOaMVmn54BWZ3c2n/GPr3L7N0MG8+Jm05k8buZCfjfcB2OaYw9LuPDJTb/ouxiOzB6xWh38j/styub4Njb27Lth6VMAtbYNM/oH8qjm+LrZ/iKafW8FVDhj26ehUIx2yG0dX5hHY5h2H/iaV5PXZXMDB7sxJlKWMNr6fAE/zOChM4zP6HSxi6n5GOkkwhe/YWqutyF2wVF4dDf8zqnnLb4Tq6sEW1ZBlowRCUAhxeocRw53seg8MZ3M7kzHf77yYU13ljLT80t7PGiSLZg/dJPXuUR7YFotT0lVbR5v6t0c1x3BNMk3CvGacliHlsD7YVf/CyCZVQFnM/ruMCaMnST6wf2vrVY7e+JUfY5U9jYvjB33lPGPUZXWkHBAS8N+uVQqGg4OBgCg4O/jfjEggEAr1R5TFwRiUZ/h/QuxC5RwQCwSdDXkPU83MYu5i0BQLBJ0NeA2fyc3CNwU7a0cPVZGyZrVv+Vuw3+f0+t9F9q9eMHXJ7+jV0l4o6hvlFf+7GrmBD7nXEZa+wi+JX9VCnvre1FPRfjeRQ51WX0Sumc32M/AxbwOHVB+phHHQdrXBmtS3q1HbGWJ2mrHW03I4bgDr1q9+1iozGoXT1uiyenJkO/D0tvzkMNm3dPO4XDHKq1h3r3535tRJ/fwW81QQXzS9WgW3KEwxrNzdmbXrTSQwpv1gWx5BVnt3YooqhNq5Q8hgeN0adekGzzdAfuK+H3Ha8iBG4Ka6cFkFXw9W967aO4mcCrRfivtz7nHOhuijjwdbZDSvbz5/L1Wh2euJzGbcq0dB/fZNdJk1UeGzNX/N4W3yBKWf3XqgN/YrdOS1C3CHM23qsObsk+qx6KrdVKRlk6FEUn0MYu8FO2gKBQKAv4kpbIBAI8hGfQz5tMWkLBIJPhv+iGruhYbCTtut0I1IaZ/8bdvtimPx+Qt00WM7am2Nra3tiuaqDr1Gr09YWXyRhCLBJIv/z7ryLJbFUGNVOysvsH7u4C2q2ljolz7YVYU1b+dAcbA2qcomzwlXjwTZlL1aAV2jpl/Y6NSUSGvE+SDVF/2XLI7idzZuznrp8A4b5pxXmzyo64Ul97zqGXjf9htPXHgyvCLYWNpfldlgKfq5GwQfQ/2VZE7ld8uhrsMVVQE3b8xn7HqcVwlP3WT3WcxU6F1E/zfoW+iU6sP/+ne74HZLW/nM9hc8Z2o46Dv1dlfE80cbRnHX93a8rge300yLQL/QV+2lbqFE4f/QYy8WNab5LbsfplICPbsh6/MajNcEmfYkx6AvcOWq5bXU7sCVedON2Emv+6lR8jmCICHlEIBAI8hFSHoNrJOHyJxAIBB+fzyFhlMFO2g8ClWRkme26VWwuu3q1G3gOlpu84Bu5naUTKV+v1RXoL4quL7dNlSgjpGl5ygVX2AO2+dswLDquJB/w4Suw6kdKCQwfruDPckAHF8xC+ONRdvUyf4aHokA8dKl+D3ZDPPgS3cK21uRMer2nBYEtsSh06cR8P7nt1PUZ2MyVnEnw1Tp0l0xzxkyMnf15PJfKYph/n6mcna/EtxjCfXUHuj2qtRL7ua9EhzJNsk6mOm8+LkeTUHY5NorlAIdxKMHciC4B/btXWBIxdcMQ97Hl98ntn50ag+1SIkopGVs4ReCSJuhSR3/xhk3uhlkH707EscdUY1njx69/A9t2K5RW1j/m45ecgXKF0wQ+hxTDUabTZOKV5R+pPPZH5zEz5Kip2+X2hkB20VSpDP/qVKUxIiNNHiIiNfk3+4jBTtoCgUCgLxpSkCYPV9F5WcZQEZO2QCD4ZBDeIwKBQJCPEFn+PiLmVy3J2CzbRa7xKq7IsXdQXVhu6GIOUZ5xE8PYw0+gS5b3F1zhRdqPFVSsMljUXvMUQ9O//mkf9P+M53Dv67FYscT9FxTW73fn79mtrEDvIqMoapANfVGPP76QtUy7BB1XtEOcKtYlCcPYFZ7oIml1hEO8E3U0UcXsQnLb5yfUooe5HYB+j6VB/J1n0Z0s1ZnHcOYuiupftMIqQBH3vOT28bDyYJvcESuu97/MKQxsLXG71IM4tcDDXzC1bsv+mFrgz6m8LweF7ALb6AOd5XatKjfB9vBnnXQGTfg4OO3C4+6wl/ffiTaoqWcVQM21yBZ2dVwe8RXYtNPcEqGr2rNrxcBmrvU1JX/CmqyqFfgMZ8Y0zm9vbodXnYvmteWO1jrVmRqiY2TQaCiPLn9CHhEIBIKPj5RHTVsSk7ZAIBB8fERwzUekTtuLZPp3Yd8jL7m66PNAvBVf9hPfThoVxtvO+l3w1vLikopy26snRk8WMGF5YojrQbDpJlXfMLUpf6czamPlxl6E/pWfWRI5Ww+jE0cF7JXb26PRtWv3ZZRSFBVZcljQfC3YhvzOEX+vy4KJLtdeAf2yKVwhZ3WZLWD79it2X0xci1LAvkDMNqfUUieeBmCkZ6WGLCvY9sHKuTEl8ZbeqA27SErF0f0uVYNuhqpr7EanroJyUomCsXL7z7IYRVjW4gn0T3/vLbdHHUJ3ziOtZsntgZEdcKyZKD35leNz6Pl2lIEeLuOowju30X3SpVcs9KUxXMlmyGyUhDbHVoW+9nnaq+FRsO24Uk9uN9x9GWwL/2gKfVutX75tJMptHaewHLn0V3b5U2cY/kQnNG2BQCDIR3wOV9r59+9GIBAIdFBJRnl+5ZXQ0FCqWrUqWVtbk5OTE7Vp04Zu3779zuX79OlDCoWC5s6dC+9nZGTQwIEDydHRkaysrKhVq1b05MmTt6/kPYhJWyAQfDK8udLOyyuvhIeH04ABA+jMmTMUFhZGKpWKGjVqRCkpKTmW3bFjB509e5bc3Nxy2IKCgmj79u20adMmOnnyJCUnJ1OLFi1IrdYvOlMhva9K70cgMTGRbG1tqdjoEDI2z9ZKnWtyuLVCgcP9zvOk3C5lirrrnsSK0D88oZbc9h2HLnVnV7CmXL7XNbA1KHgD+pEZ7Bp36hVqmZZKDGO/u419pjp9i9VNjvXjSi2KSS/BFrsVddDO/Tgr28GgOmCL+o7duTqWRk19xwN0o/MI5hPkeY2CYIvz0xq7TsiyMg6VNO1dohs17NLtody+fbYI2CrVRJe/83dYXx7sdwhsC/c3gX75qqwhR8ahy2bSPTu5rS6APwJj6yzom13lyj/+7XB/7bvA+8v9IP6wn3dCN0OHnbyeV+Vw2QJaEfkzhi8HW5aE+3LUAn6WYNoQz4OCoVilqNQ8TvF4aDvq3Wqtyj6OW7Diuy7PGnJWRMtIrPRj/Zht5j3YdVCVkkGn2yyghIQEsrGxIUPizbzReN8PZGKVezbCrJRMOtB0+T/alhcvXpCTkxOFh4dTnTr8W3z69Cn5+fnRgQMHqHnz5hQUFERBQUFERJSQkECFChWidevWUadO2c9Rnj17Rh4eHvTHH39Q48aN3/ZVb0VcaQsEgk+G/+JKW5eEhAQiIrK35xTNGo2GunXrRiNGjKCyZcvm+ExERARlZWVRo0YcS+Lm5ka+vr506tSpHMu/D/EgUiAQfDJIlLfAmTf364mJWCPUzMyMzMzMcn7gzeckiYYOHUq1atUiX1/O1//zzz+TUqmkQYMGvfVzMTExZGpqSgUL4t2ts7MzxcTEvPUz70JcaQsEgk8Gfa+0PTw8yNbWVn6Fhoa+d/2BgYF05coV2rhxo/xeREQEzZs3j9asWUMKhX5X8JIk6f0Zg73Sdj+aQkpltjb5wJpDxX2rRsJyS8a1l9t+Y7DK9c5b6OtsWYw319viBdjCinP7+N3iYBteG/22rY1Y21x1zR9shc7h/2BiLdZTHU2SwDZ0LR/4V2r04Z5qj5r2+nUN5baRTsGU9V9yatZTqRjCnRKL1U1uDWK9snLJu2Br68R+7XNmYLV6/35Y3ft6Fa5e/zoNdde7J4rIbbtKr8CmXVWeiKhiFX56vulRFbAVq4hP1lOy+AooPgbDxlsHcNpbV9MEsC39E1MfeBzi43C/Afp0b2y8RG5/Gz0QbD9W/AP6wRmt5Lbxc7w6S9HKdhq0rA/YyrXG8Hjj+ryPVvr+Aravh2Lq37EFI+T2mRpFwJa5n5+11B13Amy+Or7qa7pw1aJyK/CZzZ5d/KzF6md+diCpMEbCENHX5S8qKgo07fddZQ8cOJB27dpFx48fJ3d3PsAnTpyg2NhY8vTk36xaraZhw4bR3Llz6eHDh+Ti4kKZmZkUFxcHV9uxsbFUowamzcgNva608+L6IkkSBQcHk5ubG1lYWFBAQABdv379HWsUCASCD4e+V9o2NjbwetukLUkSBQYG0rZt2+jIkSPk7e0N9m7dutGVK1fo8uXL8svNzY1GjBhBBw5k5+ypXLkymZiYUFgYOxRER0fTtWvX9J609brSfuP6UrVqVVKpVDRu3Dhq1KgR3bhxg6yssq/opk+fTrNnz6Y1a9ZQiRIlaMqUKdSwYUO6ffs2WVtb5/INAoFA8M/5L4JrBgwYQBs2bKCdO3eStbW1rEHb2tqShYUFOTg4kIMDejOZmJiQi4sLlSxZUl62d+/eNGzYMHJwcCB7e3saPnw4lStXjho0aKDHFuo5ae/fvx/6q1evJicnJ4qIiKA6deqQJEk0d+5cGjduHLVrl12Ydu3ateTs7EwbNmygPn36vG21b0VlqSRSZg+vbT2+NT8UVRKW+24S37JueKwT8nsW3Z6Sv0yV2xsXYkbAaUN/ldsjz2KmtfarhkG/QmPO4KZdcJeIyO4u+m6apvAYjvni2K//zlVcjNBTkFp+j0+UN5+tJrfXNkIXst/j2GZmhNncnE/izZRZPLcHzUcXu5CHHLJsHqcB27bLWN148Jf82e2jcV+maLm/dSwSAbbDPatD/3m1d7tb3amCO8X6OrtyWeh4tN1czdVgYmdhBZzWVdGt768d7N5prVPBaG40y1AONfAB0YTwttA3TmZfxzp1roLtyDVOA9C48V9guzEc9S0zN96u9jeGgM3xMrq4fn+Xf0OuZ9C1MUtLkvlzNFY3OuCKP3XTonx8y1ni/triwr+j7xdvk9upyWo6qVPk2tBQS0akyEPgjFqP4JolS7Ils4CAAHh/9erV1LNnzzyvZ86cOaRUKqljx46UlpZG9evXpzVr1pCxce6VdrT5V5q2rutLZGQkxcTEgFuLmZkZ+fv706lTp946aWdkZFBGBudT0H2aKxAIBHnlv7jS/iehLA8fPszxnrm5OS1YsIAWLFig9/q0+cfeI29zfXlz2+DsjEmC3ufWEhoaCk9vPTw83rqcQCAQ5IYkKfL8yq/840n7ba4vb9B1YXmfW8uYMWMoISFBfkVFRb11OYFAIMiN/4/gmo/NP5JH3uX64uKS7ZoXExNDrq7sEhYbG5vj6vsN73Jm95sSQWZvUrNO40rb6s7ozjX7Iov4Tg4orRRs+RT7s5zk9rdzfwfb8CNcscQDpXt6Uhf13Z7Of8rtByXwAcTDFuhCZpTFJ0e9ApiSM/Yvfgr9tA6mN921A58od28XLrcPJqImuvUaa7ST/XaC7QhGcNPTuvw//eNd1GizNGzLKoj/56Xm4H7fNou/82l9XNb5DO+vxcfrg82iCZ5yjtdYU1am4n4u1w1D3m3KsYwW/gRTvD4ow7pwaXN0rYxNxwfgRYL5mcSzIPQESLbh9aQUx3Bo45oYxm70irfl4nOsaL6nAd8CtwjDgAs3J9QwMzpz1Z2saNT4TXuha6r0gt3FYrxwf5lc5fE0mXEMbO1tME1xYMvv5PaEo+3AZvGU1zN57ddyW52RTkT4fMDQyOtV9GdzpZ2b64u3tze5uLiAW0tmZiaFh4fr7dYiEAgE+iLl8So7P0/ael1p5+b6olAoKCgoiEJCQsjHx4d8fHwoJCSELC0tqUuXLrmsXSAQCP4dEhHl5bmhQWXJ0xO9svy9S5fWdn2RJIkmTpxIy5Yto7i4OPLz86NFixZBnP77eJOty33uJDKyyJYM9jSZJ9sH3cNKI3FbCsvt+C/RRSywClb2+O1nzqTVfcwesK17yMVeN5ZdA7ZGG0dAX1WQb+lNY/F/r3HTC9C/8lNFua1MRxet+915fxa4gRKRRQDeFqcf5mi31Mp4m66IQmlFm1oBmLHwWyeOlCtlgu6Jo55yVr1zO1GCybLF06S43yO5HeQRBraJ91rK7cqO+IxCt2KIkYJv8Qtr+yO+hVspHBkbnYoywsOXnLynsD1KOUnrC0Nfu7qPaYJOdr4nvJ2pzmizjkI5ovnoY3J71WW8k/TcxBKIyTB8CB+TiHKNvztnLzzxFLNGTi6LctemWD5P2xRCqeLAaz5mWTr7OXokyklJnny+zZq8GGw9zvSS2y1K8vmTmZxFy+psNegsfxW2DCNjy3dHNb5BnZpBf7WfZZDbkht6XWnnZX5XKBQUHBxMwcHB/3RMAoFA8I/4HDRtg809IhAIBPqi1iiINLlPyOo8LGOoiElbIBB8Mogr7Y+IiW06Gf2dPG7qU85IFnsAXatsXrPOaHQSXbSq1HkA/e0pvOy6kOZge6lVDL3t/pFgU1fB7GZeWnplDEYLU0w66mOPv2Ydu7wXuiA6/ML65eIf54HtUBI+A9j5kittT6qG7orL3bh6RtxyzA7o1wr3wfcb+sltZQqeuNq6tVLnzMiyx3Dv0jas046e+R3Y0px4vefvOYEtpi7q+lb3uGqKygrlN90UAQVv8/GzeKHjyxjAxz7rIsa4VxiDVYpOb+fsj0Y6qyn6PSdAa+aAn9vQFauLHB7BlZBKRaG7aYHlnLnv2RzMGmnkji5/15qze6ziCOZb3umCceOx49hja3x//C14LOCDFj0En+/sXo9ReB2vsm7d7Y9+YLN6xOM7cIdTJKjT04loKxkyYtIWCASCfIRGUpDiE6/GLiZtgUDwySBJeXT5y8c+f2LSFggEnwzZk3Ze5JH/h8H8RxjspO20xZyUJtn+x6dbsCZYosljWM6zAIcA697yDFjcH/oLZnCFl0QN+jYP2d1dbjfseRpsu/egcB33Q7zctjiIGmQf12PQP3+FK4/YmKJ/9auqrO9+/1d3sHkXfA39wt/dk9vxaqwUozTSqp7dCyvDlDFHHb1DC65e72mGVWXup7P+vHM3plAdWAMrya9eyz7dyRVQ77bTCqeOqY2+zeZ2+HxAbcaa9qTOG8A2+kBn6BdpzGHt8T11au2d5/QB6gFY0fyvJViRPr0hH4euvufAdmqQVgpcY4z47bVhB/RX9Wsjt/02oj/8pjuV+fsa4z6wv4A+1FHX2P/c2A1nk6N3SkBf6sT7tqk3Vpy59iNr45okrFjU7md8TpPObv/kdl3nOUM//o2pQjj9hEqVQffJsBGatkAgEOQjJMpbtGM+vtAWk7ZAIPh0EFfaH5HoOkZkZJ59G/ljrR3y+2vHtILlDtfjEOXxjbaDreH3WJtyaUyA3O7vegRsCq2/3i0XK4Ot1NZ46D/RqszifAlDwS+mFYG+xTMtN6yStmAb48+h9L/3RXeylkux2smi+ZyRr+qoXWCbqlVpp/BRlComp34L/bQxHOKdmI5uhUnPOLza8SGYaMMcHJ97RLzcjqlhB7YpQ1fJ7d9eVAPb6UgM07apylLGlBvNwObqg6H8sT+yXPFwIkoOtsfYTa3QOHT5y7JD97d4LTnptx1YmNlYKxrdvx2Gie98URH6kT34pCmnQskqI41lH4U5yg+dB+G5t/gkZ0LsVPcM2IqZY2bIZdP4PAizLQU2z5W8D+pNQ7nmQgvMU5+SwPJJ/XaYemH90dpyu/1MHk9GchadrEWGzWdwqW2wk7ZAIBDoi6RRkCYP0Y6SiIgUCASCj4+QRwQCgSA/ISmyX3lZLp9isJO2IktBCuPsHbs8lHW82MaoD9rcYR2vtgWGbP+poy9rM3pMX+gbVeKDWGIOVj65/7U99NVp7DKmTEQ3vv/txMrkfTpzGZy9g+qCbdd4Dqd+VgN12JcqTN9p8YpFuParsTp8oVus0TqNw33wZKEP9GeU2CK3+88LBFvr7uz+Fl4YQ6/XVlgD/aDvBsht20cYC/7zMHZf7DEN9fdTT8tC3/F3Pp53euE+sDqOzwAkBX/PthpLwfbCjzXah5mFwLZmXGvoO//Op/2zWqiNk1b64fDHmM50RFlMQdvF+azcvpCC7oFW19il1P4mPmfY/kdD6Hccxbrx5kM1wVbkjwzoG7vweeD+iwnYntXk/gSHs2A7sguf07Rsw9/5+91KYFM4sVvmH7+yyJ9duQZTxRoaIrhGIBAI8hPiQaRAIBDkH4Sm/RFxKPWSjK2yK1A8d+Tb5PX+K2A5+6Z8K9dqKUZ9zeq9EvrVbB/K7WcZePvvcIWj1G71sQPbV7XQDWvbjYpy+04vjMwjF4z4O/6K5YnIdri7TU4Wkdum1eLBlqBCqcCyD0c2xh9C960kTx579GzcruDpuA9mRDWV25k6Qw9fye55CaVQhtrjXQH6wct5vcMnYZY468fsYncj1Q1ss9ushf4QV65E9HO138C2dEt76Kc58e3/jQxXsM24w5JDA3csCFzgPmbgiyvH55NpIkYnWkfyJVhWLMoz801Q3mrtdVVu71xfG2wtu3Lk6cFFKHkk1k+FvnYkb0hrjAp93bwA9Gfs56pAtnfxfPI4zOsd+SXuuyydDIq7DnEFnHUdFoJt4GSWzdJbxbMhNYNoPhk++fgqOi8Y7KQtEAgE+iKutAUCgSA/ITRtgUAgyEd8Bi5/elVj///gTVXlSp2nkrFpttvUiy/ZLavEagwbv9ODXb0qVkB3txsxLtBvXozD2otaYIj07q5c/aXs/26Cbd8OzPJnGcO7zOyr52DbUuYX6BtruZB17BsEtuD5rAt/v7UP2Lx3oO7pOIMzrz2ejZnfnmplkXM9glVRpB64na8vcCY/Vz/MCJiwg/Xn+C8w9JsyUfvd2GSJ3E6RsGKQiYL18KNJZcD2xywMG39RjcfeqnoE2AoYo7vbwTkcQ23fA7M9amPcFfX42zNR//aZzs8dnjTWzRbINpMXeAwyXDBz3vOqXPW7WQfMDHlyJmvGL1vgc47i07D/uAW7lA78Bl3qVk/DtA0Wr9h9cOy8NWAL3MwVhDRm+LN2L4cV4X1s+bw49BceI8czfC3XcehBuZ2erKJgv8MGWcH8zbzhsTiYjCzMc11ek5ZOUf2DDXJbckNcaQsEgk+Hz+BKW0zaAoHgk0EE1wgEAkF+4jN4EGmwmvbiiCpkUSD7P2XVEA5jf9QOl//e7zjb0jHcPC4T02VePslacIkvH4JtpOc+ud3zZC+wORdKgH7MU9ZBPXaj1tsldC/0H2c4yO0Aa9TKQwN7yu0KUy+BrZg5atFhL0vL7TvhGDL9U6fNcjtLQk17UzdMqTppM6dN/X7+YLAlluYwcefjuJ7GI05A/+QLDvG2MkH9+39FuWJ3rfXDwTaqDabPnbaLj22xqqhT37mO1cbNXvCYMpxRt5YsuD/Y7xDYluxoCn3v6vw9nd3Og23iafaDrlz8EdiiVmBKgBf1WXN3dsJzpI8376+VY9uC7VkdvDXf13aW3G66FVMUkM5dvNUTPt9U1dH/PKTCDrk9/cdvwNZwLB6/nnYc5l7/2CCweRfmdLmPLnHqY016Oj36cZxB6sBv5g33+ZPyrGk/GTTeILclN4xyX4RZsmQJlS9fnmxsbMjGxoaqV69O+/bxZCdJEgUHB5ObmxtZWFhQQEAAXb9+/T1rFAgEgg+HQsr7K6+EhoZS1apVydrampycnKhNmzZ0+/Zt2Z6VlUWjRo2icuXKkZWVFbm5uVH37t3p2bNnsJ6MjAwaOHAgOTo6kpWVFbVq1YqePHmi9zbqNWm7u7vTtGnT6MKFC3ThwgWqV68etW7dWp6Yp0+fTrNnz6aFCxfS+fPnycXFhRo2bEhJSUm5rFkgEAg+AJIerzwSHh5OAwYMoDNnzlBYWBipVCpq1KgRpaRke7KlpqbSxYsX6aeffqKLFy/Stm3b6M6dO9SqFXr+BAUF0fbt22nTpk108uRJSk5OphYtWpBarX7b174TvTTtli1bQn/q1Km0ZMkSOnPmDJUpU4bmzp1L48aNo3btsjWMtWvXkrOzM23YsIH69OnztlW+k2UP/MnYMtulylKpVSkmHP9n/mfCbmAFT6HrmU0H/KeTPDkjX/pEdAOzWsW3+FbW6JJltgBllwmz2S1roqoN2I7FlYR+S0euQLM8Gt3dHMZFyu0jG7DCy6GaeLtttp9v4ap9i3cvE7d3lNsuZ/AEiOqP/a6nv5fbFdphuHd3l1Nye+rJHmB7kYlZB58dZ+ni525rwDb3JRcF7tj0JNgclMnQ99rHEoN6jxPYpK74y/pfTw637rsYMxSaJvF58aA8Zvmzuw1dKt6Ib/9nrcJwb4UvH3trE50Mexk4nvF+XHlo0kGUQNK9OOQ+tjKes+HtZkC//RiWkEzboEtrJTe8EjOrzi5/4XdQrpk8s5vclnRSFLiaxEP/uVorTUIiZgscUfOA3B4azoWp1Rn5wOPiP/Ae2b9/P/RXr15NTk5OFBERQXXq1CFbW1sKC8MMkAsWLKBq1arR48ePydPTkxISEmjlypW0bt06atCgARERrV+/njw8POjQoUPUuDHKmO9DryttbdRqNW3atIlSUlKoevXqFBkZSTExMdSoEacmNTMzI39/fzp16tQ715ORkUGJiYnwEggEgn+ERo/XPyQhIfuCyt7e/r3LKBQKsrOzIyKiiIgIysrKgvnRzc2NfH193zs/vg29J+2rV69SgQIFyMzMjPr27Uvbt2+nMmXKUExMtvO+s7MzLO/s7Czb3kZoaCjZ2trKLw8Pj3cuKxAIBO9FT3lE94IxIyPjbWvl1UsSDR06lGrVqkW+vr5vXSY9PZ1Gjx5NXbp0kR9yxsTEkKmpKRUsqBPMlcv8+Db0nrRLlixJly9fpjNnzlC/fv2oR48edOPGDdmuUOBthyRJOd7TZsyYMZSQkCC/oqKi9B2SQCAQZPNGHsnLi4g8PDzgojE0NPS9qw8MDKQrV67Qxo0b32rPysqizp07k0ajocWLF+c+3Fzmx7eht5+2qakpFS+enf6zSpUqdP78eZo3bx6NGjWKiLL/UVxdWS+OjY3NcfWtjZmZGZmZmeV439Y8jZQW2fcwz3uzq5ezNWqiKfv5ytyo1UuwWf6A61QP4e953Acrzgy/10FuO+l8R53Qy9Df0ItdyEpOeQq2s9ew2smt61wxu1Y3DNMubskh8Mp2eL8WNRf1SsmI9dSbK7D6y/yx7MY3Lbw72H4LwAovY3pzxZ4floeDrc9BrtxeOBXHs/8GhjorfHj/bXlZBWzn9/EViK5r3sm9mBIgYQg/pLZcj6lQPfajhtzThl0xVcWwGoxDBJ8jYburgs3rJj4IP7fwC/6OU3iVk1CP29Gp6Ar2vAVeha0N4oo4nka4v2al8UOodV9j6tMug4ZC/6VWhfPfqmAq3V5/4bMF4/12cvvcuJlga7WX3QWjG+B+X7wSq/e4nOEQ/fIhD8G2J66i3DZ/ycdAnWlQ3sFvJa+eIW+WiYqKApe/t81Fbxg4cCDt2rWLjh8/Tu7u7jnsWVlZ1LFjR4qMjKQjR47Ael1cXCgzM5Pi4uLgajs2NpZq1KiRY13v4x9r2m+QJIkyMjLI29ubXFxcQJDPzMyk8PBwvQclEAgE/wg95ZE37stvXm+btCVJosDAQNq2bRsdOXKEvL29cyzzZsK+e/cuHTp0iBwcHMBeuXJlMjExgfkxOjqarl27pvf8qNeV9tixY6lp06bk4eFBSUlJtGnTJjp27Bjt37+fFAoFBQUFUUhICPn4+JCPjw+FhISQpaUldenSRa9BCQQCgaEwYMAA2rBhA+3cuZOsra1lDdrW1pYsLCxIpVJR+/bt6eLFi7Rnzx5Sq9XyMvb29mRqakq2trbUu3dvGjZsGDk4OJC9vT0NHz6cypUrJ3uT5BW9Ju3nz59Tt27dKDo6mmxtbal8+fK0f/9+atgwu2rIyJEjKS0tjfr3709xcXHk5+dHBw8eJGtr61zWnJOHN9zIyDw7skmy40i9jAx0TzLXCsZ7FYki/4xDWOXGQ8meKYNqdATbwx5F5LZTXZQ8Dk3EqiSNl7KssGMBVjMpYIf6VNH2d+W2bnHVk1pefU4RmFHOeHws9I207vlibqHcNPFuC7kd74f7J13C/styfCUx4FxXsJk6sLubzRV0PYvvglFmbYpekdtVrCLBdrEiS1YW51HyCF6Ax8TSiCWHXsUwQrNIE1xveavXcvuPsxXB9upLPkdaVvwLbPssUb5Rm/O+fN0YJRCvGSyzPOyDkZ6TqmGR4nmH+BwyzsJ78iJ/8L7skYXuierauKzXXh778AodwDagJEpYodF8rDcmomTVcTy7pq2fjVGgLt1wXyY35vPg+XaU9O5rnTKmzVhyVKRmEP2PDBoF5VEe0WOdS5ZkZ7QMCAiA91evXk09e/akJ0+e0K5d2edGxYoVYZmjR4/Kn5szZw4plUrq2LEjpaWlUf369WnNmjVkbIzRx7mh16S9cuXK99oVCgUFBwdTcHCwXoMQCASCD8J/4KedW6aPIkWK5LoMEZG5uTktWLCAFixYkOfvfhsiYZRAIPh0+AwSRolJWyAQfDIoNNmvvCyXXzHYSfv7gCNk/neWv60TudK21TN0ZVJOeSi3R3v9AbbxA76HftPpx+T28yZeYCvX/JbcXlvkANga90Wd8VRdrvDS7yRmrdsa8wX0Z3ixvWEhzOBm+YyddxKLoGY8tuhm6E+axq5fBXWOmq8fu62dlFDv1s361+U7fnq97BJq9W1KsRbsug3D6DdPawT9Tc1Yn69T7RbYJvrultuj0r4C2/IYDOWPXMhh/6rSePnzYmUR6Dcdc01uK6vjebDrFI/n8it0x/I4nAX9h134ewoewqr3yZzUjgovAxOFVOsE/UIveb2+wVfA9iCZvQfUF/Bc0+gcP+sJHKr+cAvqyxvuY2h/o2CuAF/UDKsmDdzH50jDPpfBdmNaeegXHsbPWlRP8JzpPpmP39pH7KKpUqKbpUEirrQFAoEgHyEmbYFAIMg/6Btckx8Rk7ZAIPh0+AxqRBps5ZoBJ9qQWYFsh9Hmtqy1/vA7pnhd3oGFx8EL+4ItqwCuW2XFm2pUFEPVB/oek9uLNmIK2kwb3EU297md6ooHv3rjq9B/lsJ+ygXN0Rf76UwOVU9zwODUQhtRIw04w37b5+NRI00czRpudA2s1uN4Raeq+nCuiPM8Af3n06O52nih8ziehkMxxeq231kPV2ImWzKuw/7U3YqdA9uCU/Whb2TBOmn1YuhLbGeCqQauTqogtyWdWN6q4y/I7TQNpuh9mYFV1B3N2AddrfPjTchijTtuSGGw3e2G+7bk/9jv33oR+tUnDOXnHl+uuAg2bzOsSrTuCevGiRvwOzOtcXw/9GG9ee7uFmDTdj420sl7FNgOn/fsa8PPAFJKOoLtRSW+lnM7yQdXpUqnE8cnGWS1lzfzhndwiBzf8T406ekUGTzWILclN8SVtkAg+GQQ8ohAIBDkJ8SDyI/Hrpvl5QKdW5O4qku/FgdhuQErWRJZPhCzqcWo7KA/ci/nQKnp+RBsa6fzrabaB4+oY1m8nR3RmscwZiuGgoffKgH9IVW5yOzuHzDknbQ8reLK4HcabXaD/q6nvPCrsy5g85rEhWoLzsfPfTcfXRJPJxaX2wFOd8G2UcO3zFIn1Dx0Q9U3F9BKTVcJs+jVc78nt3+PqgS2CXV2Qn/OMq4cY18aQ+f3H8Twc2VFvv9XWeD+OviYsylWc8UCwbUL3oP+sl+ay+2aX2FB5ZgUvlVOD0Z5pvhk1GTclnEa4WNnMLdy0Gqunbp6QTOwVe11GfqJv7Iksmj8fLCdTysK/dWztM7TL9DZuMh2lppS3FAi+t/y5tC3Kccuk6VGXQPbEHuWIxcd5eOjUOUD5+a81n8Uk7ZAIBAYAOJKWyAQCPIPn0NE5L/Opy0QCASC/z8M9kq7+LIsUv6dsrDCcnaZ2hmMuWfT27DueDQZU1Vu3FgP+t07H5PbB0LrgE3d9ZXcLlYA3QFTs1AfDJnJOnbxzTfAVvwQuvUtvcXar/EI9MPSnGYXMpcyOnXiVmH4co+JnBa0mA+GL4+axiV6EqqDiaZsxNBru9t8iZHZ5TXYsjL5dLBeim5QQ79C7V4qxCHcrmvRFe5mEGvuryNwO9YqcYB1v2GXwJZ2qC/fCCsH/fEruULP5ld+YIvswJr/3Yp4Hjy7h2HtDdbydz5JtQObZjGPd+DPW8EWUhf3ZU0L3n+m8Xj9s20op14gnZz5R+6VhL6kNdwuWweCTV0ALwnN3VnX71zjNNgOn60pt7PwkJD7DizjF9We0+fem4D7K3M+a9waU06DoDHSL4XoR0HIIwKBQJB/EC5/AoFAkN/IxxNyXhCTtkAg+HQQ8sjHo9L8q3IY+5+jWL98XRV1NbdtrDc/K2kHto5aGjYR0b6nrN2ZpqFWGB3N4ebxyfZg01hjGlBt69M16Bd9MwJj50fV5vDhJ5m43l/j2P9cCkff657jMT1s6Cn29Z1SC32vjbQi1W3QnZp6DtoL/fl7eT3qBzgebSzGP4J+W2vU0bdFsE/3qzJ4GmWeZb20dbMzYLsaj/tr72GunH5zG2qrSSWwyOrUSPZRdreKB1tkN9atbSLx2CaH4C9UE8/7unxBLC33V3VOLfDLE9TfKzS/Cf21xzmU39kP989DF07N+rXfn2hLxaKv8YN5/92a4Qm2hbV+hf70Id3l9sEnNcFWbSA/E3jYDFMUPOrtA/167c/LbR8LDMGf94BTDdj+FC23jVMyiLD6mcEh5BGBQCDIT4grbYFAIMg/iCvtj8i5cZVJqcwOY28yl+/Jlp4OgOX6dtsmt2fO13Fvu48VSxr/fEpun0z+EmyWkXwrbopR2eT4F7rqxfvwbpvhuwVsQ9RY5X36Ub6lt72J0o65ljrh0+Q+2M7GoZ+YuQ2PYcF9DId36/VAbr9YhJ/b1QeXtarELmMD+6PMsuguV5WxVGJ2wP0PS0PffR+7uBln4n420qpMfugpSgzOZ7AijuYbXjZxAoaxJ59AqSlOS3Z5WQ592sZ985vcXjmwLdhiX+N6mnmym+bREJQYKg26I7cLmqL75sGrZaFve4eP53NbrDrft+ZRub1mS0OwaUxwxhh34ne5PeUSykdrY3B8qY78nWaJKANdm8LVabIaogui/S2sOnOvB4fHn6yN6QKmDeeS6w+zCsnttGQVHSYDR/P3Ky/L5VMMdtIWCAQCfRFX2gKBQJCfEJq2QCAQ5CPEpP3xeF7ZjIzNsnXmBBVXE7G7YgLLTSvUWG7bxKJr3ktfXDZZxbr1g86o+Zm85KOYXgbTklq3jMdlV3PI9OSRvcCm9NAJ9fXhMVX8BqvanH3CFWh6uqFb2BKf4tB3r8Uubfe/RpexLE/elrjmqEUb4S4gdQrrz/Nuo97drTiHd6+9g5q/wzqs/tJoEj9nqFsAQ/m77e0nt52K6VR0eY1VUhrU5DSg3R1xH4w3aQP9qPOcwrSmK/o2/niUq76XGPUMbNJd1Ik3XGE3Q6NqYKK09ZxaN84P9+XM2puhH3KWQ/vNb2O1FCOttKmWMThDDB7yO/TX9+K0qd6TX4Ht8nFM9evSmdMdpKvw51vAnFM6dHDBc23xb5iatflkTg3ha4Eh7nviK8pt7TD/rJRMIjpFhsznII/8q4RRoaGhpFAoKCgoSH5PkiQKDg4mNzc3srCwoICAALp+/fq/HadAIBDkjqTHK5/yjyft8+fP0/Lly6l8+fLw/vTp02n27Nm0cOFCOn/+PLm4uFDDhg0pKSnpHWsSCASCD8ObK+28vPIr/0geSU5Opq5du9KKFStoypQp8vuSJNHcuXNp3Lhx1K5dOyIiWrt2LTk7O9OGDRuoT58+71plDtSWEknm2Xv23FB2SVKhdxJZ7GBXq2dN0PXMawv69ew4wffCdt7xYLPZyVntBszeBraffusCfWutYLMi3bEqisUoLMwqKVlWuO6NUY8LKm2U24cT0Z3s/oaK0De5zS5uZqg4UMEt7NJWcwbeFtezxSi+Eee1KpEcLQi2jUa8cxVn0IUtUyfb3IY7vOzJqZXBNnkTu0Gu69wEbF+swttr7WPbrSe6FTrvx+yKpJX079QyPBEUFXh8bV0xW2C6C1ZmWXCAx/RjCzzWW2J4W5rYYrTk3NFfQ39gCGcBDP0Lt3PFrkZy26MDrqeZFUabTviWt9NPp/jzXWvc76/CXeW2AtVAirflmWiOFWY2dK8ZDf2lJ1gaM7FHOTArnqUen7XsaqpS6VRwNkQ+A037H11pDxgwgJo3b04NGmCa1MjISIqJiaFGjfiENTMzI39/fzp16u1aWEZGBiUmJsJLIBAI/hGfgTyi95X2pk2b6OLFi3T+/PkctpiY7Ickzs7O8L6zszM9evQox/JE2br4xIkT9R2GQCAQ5EA8iNQhKiqKBg8eTOvXrydzc/N3LqdQKKAvSVKO994wZswYSkhIkF9RUVFvXU4gEAhyQ2jaOkRERFBsbCxVrsy6n1qtpuPHj9PChQvp9u3bRJR9xe3qytpbbGxsjqvvN5iZmZGZmVnO98skkLFltoZ235FF5HZVMWvcmWnsvkVZ+B+UUAw3z9IrXm439UQ3tQtxX8jtJY8CwCbp7CX/vmfl9rHlWEElrg+GvCuj+ewILbkbbDM6sUZaYultsG2rsRT6bTXsRqdJwwF9OYfvek6/xDD2W4Mwc16BSqyNK1PxzE3TciFLr4jaqmmCBfQzI/mYJIdihruQNZxOQNUOv8M0Ed3v7nflY2Yahf6J9mdRh3XQ2revKmEYu/SYcwLYK7Hy0NQlWHXHTCuqffNX6PbY/HeW8Rb83hJsHrG4T+yMuR/gjZXtw29XlNvaropERM0yekK/UQXW3O+PQl3fuAme08Zap5frCXy4rzHj49dm2SGwLV6P26Ioxfr0/Cq/gW387VZy+14/Ps6aNGOic2TYCE0bqV+/Pl29epUuX74sv6pUqUJdu3aly5cvU9GiRcnFxYXCwsLkz2RmZlJ4eDjVqFHjgw9eIBAIcvCB9ezQ0FCqWrUqWVtbk5OTE7Vp00a+QJW/Mg+uzhkZGTRw4EBydHQkKysratWqFT158kTvzdNr0ra2tiZfX194WVlZkYODA/n6+so+2yEhIbR9+3a6du0a9ezZkywtLalLly65f4FAIBD8C/4LeSQ8PJwGDBhAZ86cobCwMFKpVNSoUSNKSeEEZ3lxdQ4KCqLt27fTpk2b6OTJk5ScnEwtWrQgtVr9tq99Jx88InLkyJGUlpZG/fv3p7i4OPLz86ODBw+StbV17h8WCASCf8N/II/s378f+qtXryYnJyeKiIigOnXq5MnVOSEhgVauXEnr1q2Tve7Wr19PHh4edOjQIWrcuHGO730X/3rSPnbsGPQVCgUFBwdTcHDwv1pv5hU7Mv77YWfxoxyeW7TOC1juxl3+JysxDB9i+tRAh+ZTr7XSUQZjmHanFfvk9sJNqP+RBR7hbZdZ//Z4jv+S1q5Y4fxRBqe2nPst+vkWmvdQbke89ABb2K6h0K/ciG/HLtwrArYMDR/GjCWuYFMWRD9f00TeliyMTCflMfbNXhW0GGw9szBcX9J6fvCFI+736+fY/zvJA32trzrjdpq84rGb+8aDLdYft6VrIT5GyzY3A9vADvzDCv4FNey0kpiW9FSz2XK7hscQsD25U0tuN2gWAbbyHXE7Qyd1k9vWT/BZRqe5x+X2ukt4rqk1+FA+LpP1+cjeeK55rsPYgyf1WfcftmET2CaP4GN0+GUpsKUVxvO0T6UTcnv4lfZgszTj8H3TSHY4UOcDN+3/D++RhITs9ML29tnPUXJzde7Tpw9FRERQVlYWLOPm5ka+vr506tSp/99JWyAQCAwGPa+0deNC3uUYIX9Mkmjo0KFUq1Yt8vX1JaK8uTrHxMSQqakpFSxYMMcybz6fV/5V7hGBQCAwJPTVtD08PMjW1lZ+hYaGvnf9gYGBdOXKFdq4cWMOmz6uzvoso4vBXmlbV35JxpbZ/3jPk1himPdXPVjOrTDffj8dVgxsD+zRfSrDhv+jkkvh/9X8rVxhJtMFb6cdIjBzX6KCb1F/nPk/sPU51gP6Njd42aiBeH8Z/Zzd39Lv24BNrTOG+FEsK3hOwExwp0I5PD+xKI5Vqo7ub1nX+Xt0bxFHd+Tw81E/9gVbjcBb0L8ay9LFzSAMwU/x4m2uNuAi2G4PwWNieu+x3H7SGY+fpMQBaksi7ofR/W7nRY7OnTZ7Db2P3SmcOc/GCfdPchLLARfmVQLbpUzspxbhH9vLxjqujR15O5fu/QVsU4Z9C/0Lbfl5j48Huk9G+qOcZKSllvTf/h3Y1M3Z2Mr+AdgeXsOskfv+CJDbKW1x7JLWxWCmN5+zmrR8oI/oWbkmKiqKbGz4N/G+q+yBAwfSrl276Pjx4+TuzmkCXFyy01O8z9XZxcWFMjMzKS4uDq62Y2Nj9fasE1faAoHgk0HfK20bGxt4vW3SliSJAgMDadu2bXTkyBHy9sZYCG9v71xdnStXrkwmJiawTHR0NF27dk3vSdtgr7QFAoFAb/4D75EBAwbQhg0baOfOnWRtbS1r0La2tmRhYQGuzj4+PuTj40MhISHg6mxra0u9e/emYcOGkYODA9nb29Pw4cOpXLlyOXI45YaYtAUCwSeDQpJIIeU+I+dlmTcsWbKEiIgCAgLg/dWrV1PPnj2JKG+uznPmzCGlUkkdO3aktLQ0ql+/Pq1Zs4aMjXUKp+Q+dj1G//9AYmIi2draUq2ACXI19hcV+JYlqRS6QJUszlVK7l1E/c84AwV+Wy60TeYJOg7t/diVMC4VQ7YzbmGa0rr1L8vtsNMVwOZYHPXmH0vsldsLvsVK7a/L8vfoptlMc8KxH+o7XW7XPDYIbK5O8XJ7egmsDr8tDlOY7grjsHuLUvFgs1nP2l6T8eFgu5KIoditC12W2zMWdQJbUH8ew5Q97cBm+QwVOa2iRJRROg2XtUI3utFlDsjtOTNxXya78/4yx0NAlrEocobPWiS3W9fDsd8cYSe3ba6hu2LYsBnQ79gnSG6rzXG7ntbn9hfl74Pt4Vof6JtpuWE6DHgItutR6PZoc4p32I9B68F2JY3P/0276oDN4Tr+zN0DOez+zuaSYGv/3RG5vXkNP0NSZ6TTrYVjKSEhAXRgQ+DNvFHxm6lkbPruvEhvUGem0+X14wxyW3JDXGkLBIJPhs8hy5+YtAUCwafDZ5AwymAnbdPX6aQ0zt6zRVpz5Y/aDphNzVmZILfnJhcAW6EpeHv7w/qdcnv4Cby9tknjW6qMmyiHZNmjdnFyO7t+SSXwFr6BGyaSGb6VXQBNh6Ijv9FJjoSb2X8F2G5koBzx7T2+jTe/g7d/pUpw5Ge3P/qBzWe9juTwJcsIxo/swKZQs4yw7SHKPk4T8VT5bTZnV0ytngK26es5ws7tKroulhyLlXVSVHyMTl9F2aCwG0a0runNkapOcRh5mtKBix27HUZ95F5Xe+hXOM3HJHg3Zl6cE8xRq5bP8dhm6iiJBUZysh9NB9zOhqPZlfCvl3gsTVJwPa/LsLSiWOAFttKBj6Ef14rlkZ+nYuTnqHG/yu379bFi0SnPotBPjOUqSpJOZOz2hVpVbbQuSY0yDX+mE1faAoFAkJ8QV9oCgUCQfxBX2gKBQJCfkIgUeYmIFJP2h+d+Z2sy+jvLn9ck1vHWVENtLrTXGrmdloEa9v2OKNYt7ttBbvtkoAZZfG6k3I4vhC5/509ixrTeXTmj3IKT6Bh/ZCZGNzUbylVlwrZWA5v9TXZfnHK/BdheHkdXL5OqcXK7zVcnwbZtd025XaMBapl2fqhpp0yrKLctYjPBNmz1Bv6cEYaJL1xQH/rXN3BFHDMsIkNpTvyrWTt/NtisjNCVMWDpCLmtcMZnB7GbPaFv7s6/tMRa+KV2t/k7Y+qghq2ywl+oizVr8OO26+R5L8/NQS2x+svZdKy6c+Mv1p+NRuNqXi7WCnEPwP3cbOgl6PcrdExuD+45AGxZowtBP7Ytn9OWdrgvX6jYJ/hPnecDlg+xKtCy7zn9wqkSuOye0Xys47WqP6n1zJHxUZCk7FdelsunGOykLRAIBPoi5BGBQCDIT4gHkQKBQJB/UGjypmnnSfc2UAx20lZbqUmyyNY4I9vxMJ1O4d5e3OUruZ01BGP469f5C/qninF2LtVlO7Alz6wot4dN3gA2C38Mnb+QwFpmudLoRxvliuv1NGN/4kk9Mex49j3WU9eXWge2XrODoD/nuzVye08S+lDv7cHh1b0CseJN7Ev0NX7Wh7X8krNR7x4cwb7gmkh8HjCs1S7oW3Xn9T4agGk/n/qzttrrhyCw/bh4NfQLH+cxvBqBOrraE0PDYx6x/7xkg9uVUoyXNX+C+q3PRvQjf1KPnxeoXVBHb1b9stye/xdWaq9VFMPRTZK1fN5TUe9Nc+S20Uscz51B+Ixk7lw+v5M8MMtchh365Be6xOd/vdH4bGPm5YZy2zQWf9rmr/DScsxd/t0YzXcEW2IRrd/bRT4+KlU64RMTA0RcaQsEAkH+QWjaAoFAkJ8Q3iMfj6E1D5BFgezhbRrMFUuiv0S3vgqD78ntyEh0XTLW+Tt1WM63/I9ao8tfvban5fapJLzdP7etPPT9O3DB1wtRmFnQ6gSG0m9M44Kdph2xKomiF4dpn03H9UhKvN3utD5Ibrucw1v6lc05o9vOBfPBNjoSs+w5pbGr3OPmTmAz0fJEM8GIe1q0ujX0M75gycG4KUopGjPe78mFURqYE9UI+j+tZbkkaAaG4Ksa4CAKH+P1xnTAfVBkJfcTiuNprZkaB/20J2xvUuYG2H4oxNkNPcwxVL66FaZQCLdjt0e1OR4vo0zuex7Ec410POcyNCzrNRyGkoeraTz0Zx3h38LpoehC+sdqPvYdzo0Am2lbPPfcCnD6h9tF0L20ZBeuUlT0u5c8zuQsOl6LDBpxpS0QCAT5CPEgUiAQCPITQh4RCASC/IOQRz4iVc0fUgGLbDeuGQ20dVHc2/XtWJOMK4yhzbdHYZXwqjMvyG2TQeg2d8eX9d3k7zEMusBs1APDHnClD3U0fmdGQeiSz9dcLicxE923Yn/nMO1LP2BKzmMrMFVr/6dfyu0bZ8qBzagAa6ZLXwSA7f6fuF6FmgXVmq2vgO3IDXZFK3ALnx3Y30RdNqm2VpXueNS0W/RiXfZKAqYlvXG+CPQDd/WX26OCfgPb1KtNoZ/iwm59s6puBtuk0pxu1eIl3vvWLXQH+q6WrOcG2KIT25EU3gfbZuikKLhTHfptF52nd1HQhN0XN7+sB7aUiqjH373K55OdcxLYVCfwXDTzY3tcCXx+0vpcX7n9/Xf7wbZxVmPoX2jIbpne7aLAFurO7p31DgXJ7exq7JjK1uD4DFz+9KrGHhwcTAqFAl5vyscTZVctDg4OJjc3N7KwsKCAgAC6fv36Bx+0QCAQvA19q7HnR/SatImIypYtS9HR0fLr6lVOaj99+nSaPXs2LVy4kM6fP08uLi7UsGFDSkpKes8aBQKB4AOhkfL+yqfoLY8olUq4un6DJEk0d+5cGjduHLVrl+1mtnbtWnJ2dqYNGzZQnz599PqeBc/rkUly9i36wU4c8dd63khYTrvSyIvKuA6TWvifFHWAJYaikzCSMVWrgkq5DejaVcI8BvqrH3EmP7UNRttN9MHIwQl3W8nt2JdYQNQ5jk+c+CzMLFhtLLq/qb/iaixxjXG7bE/zZ4/d/AJsjrVx7K+TWMo4/gBdGz128XpTHfCkjq6O0aZFtSrZ3BmI0YnR6Ry52MP1FNgO+8fjeDJZXhp/rhXYKng9gf6jZpwtLyrLAWzpTdk9sGUxzKK35vqX0K/m9UhuLxyJhX2f1eTtVH+JMkaGLcoRdy7xvj7UcC7Y+nXmbH31Fp4DW9gWdNUz9mOXxIQ4lJqU9ngcvOzj5fa9qhg9qczisT/OQFkluVky9P09OKtlJ8ezYPsqlN0FC2q5J6oz1YRHxAAR8khO7t69S25ubuTt7U2dO3emBw8eEBFRZGQkxcTEUKNG7IdrZmZG/v7+dOrUqXetTiAQCD4YCsqjPPKxB/ov0OtK28/Pj3755RcqUaIEPX/+nKZMmUI1atSg69evU0xM9hWds7MzfMbZ2ZkePXr0ttUREVFGRgZlZPCVWmJi4juXFQgEgvciXP6Qpk35aX65cuWoevXqVKxYMVq7di19+WX2LahCJ1G6JEk53tMmNDSUJk6cqM8wBAKB4K2I4JpcsLKyonLlytHdu3epTZs2REQUExNDrq4cFhsbG5vj6lubMWPG0NChnJkuMTGRPDw86FK0OxlbZmt2348ZJNvtLTHjntlLdj2LK2kNNuOKCdDPesD2Z3vQFc72IeuXV/9ELfNUDT/ov6zI2mGJgAdg63OsB/QL7+dlQ0M2gW2scVu5fX5VRbC1HHIc+tsesIuiqQtmw4s3ZU27ZImnYIsKw+1s1ZGlqusJGL5cfgJ/to41VpUfM7s39G//wFp0S9/LYDs7r4rcbjfhAthujUE3TNUIDhW3OY26vkMJ3M7LrznUf40C3e8cC/CzhU138OGG/V5cb7lRvJ0PlSXBZqv1OCPFHXX89t8fgf6qowFyu+HOYWCzCODP3jtZBWxfdTgN/X0PORy+yAa8wHldEvtFrTmsPPYapj5YN3ih3B7WA5+JFBiBz15eZbB27qHE38k3gQfk9uFWnMJBpcmga2TYKCSJFHm4is7LMoaK3pq2NhkZGXTz5k1ydXUlb29vcnFxobCwMNmemZlJ4eHhVKNGjXeuw8zMjGxsbOAlEAgE/wiNHq98il5X2sOHD6eWLVuSp6cnxcbG0pQpUygxMZF69OhBCoWCgoKCKCQkhHx8fMjHx4dCQkLI0tKSunTpkvvKBQKB4F/yOVxp6zVpP3nyhL7++mt6+fIlFSpUiL788ks6c+YMeXll34KPHDmS0tLSqH///hQXF0d+fn508OBBsra2zmXNAoFA8AH4DFz+9Jq0N23a9F67QqGg4OBgCg4O/jdjIiIi9W1rkv6uxt5wAVfF3vEE06Q+jmWf4C4VToDteDDqnjGdWSNd2O4XsE3p1lNudz0eAbYfjxTBwUkc0n07BtObli72DPrJWe5y+1EmVgixKcBVQSRjDIc30jmrMm/wdg5pi77gqyJZfnr8GuPoPQ9hYNPvpbT8uBMxbWp8OIfVn42rCrYhizBsvLAJ+xYP+h/64KfW4ucOA/f2BNvI+RgG/dtT1ns79wsD29ZZGEZu25a/c3KpnWCb9YhdTQvZoE/ya3e8aLA15vPgaUO8T55Zd6PcTtJg2oHweNS/i2/m5ynKKbFga1qfI4ET1KipR6bieWCq5PMpqwDq6Lq+aRHLK8rtHT9NB1un8exfXWoaRiLPd8Ww9tYbWINvdj0IbF67+dzTlOcBqLLSiSLJsBHeIwKBQJB/EAmjBAKBID/xGVxpKyTJsEafmJhItra2tP2v4mRlnX2rOGHkd7Ld+i66J1kvfiG32xW6CLYJv3eGvnZR1KdN0a2PVOxIY3ML/8tKtkf3t2fJtvQu7C3QTS0pk0ON1YvQ9dEok3d9t1koG6x9jNKOchaHbXtNwvFc2IpZ/7TxaPYQ+o8PFJHbur6q9jf4Nv15dyz6a3EMJYZu/fl220SB+zJd4v13NQmz/E10+wP6vb8Pktu+IViIWTe0v4QVSxDrb6F8Y2bKY59YFvfl3Qzc7xvu82cL/Yyh4E+H8XpWVkIJ7XoGbou1Me+jJY8CwBZ92k1uO17BHf20MfY99rAEUf5H3Af775aGfp/yLAGu3IZVgJwi+Dg0n3IUbEv/xCLFBZxZQkq7jx5bjfwvy+2wI5XktiY9nR6OH0cJCQkG5+X1Zt4I8PuRlErzXJdXqdLp2NkpBrktuSGutAUCwadDXpNB5eOEUf/KT1sgEAgMiTcuf3l56cPx48epZcuW5ObmRgqFgnbs2AH25ORkCgwMJHd3d7KwsKDSpUvTkiVLYJmMjAwaOHAgOTo6kpWVFbVq1YqePNE/BZeYtAUCwafDG007Ly89SElJoQoVKtDChQvfah8yZAjt37+f1q9fTzdv3qQhQ4bQwIEDaedO9nIKCgqi7du306ZNm+jkyZOUnJxMLVq0ILVa/dZ1vguD1bTL95hKxqbZ2pTajDU/t12YUvXuDE5BabcP01qunzQT+k2PB8pt5WPUvRQ+rPG5rEVbg1B0JVy3m/VBkyT0ycq0wd1ZLYAro1gpMYXp3bEcvhzVACvFqGzwQK5u/D+5rdbxA5v3pKHc7uKCaTZfqFCvW3AlQG5bH0U3Q7uOHN5tNhj15Fv97KAvGfF2Fi76EmwBLhwL/m1BDNnu36k/9BUq1neVM1+BLU2FLolFrdn+PB019si9ReW2S2OsxPJiB4Z7Jxbn7zQvjO6Btts4/WqPH1Eb//XHFjh2rVtsTV/cBybGWsdvFrqFDl+4DvojV/aS26Y6+dLUOvKs9WNeb6ozugc6t+PEbHf/wm02e61zfaZ1mko6IqlSa5cUvMsavyornc7uHW+QOvCbeaPuF2NIaZwHTVudTkcvhv6jbVEoFLR9+3Y5dQcRka+vL3Xq1Il++ukn+b3KlStTs2bNaPLkyZSQkECFChWidevWUadO2emAnz17Rh4eHvTHH39Q48aNdb/mnYgrbYFA8MmgrzySmJgIL+2Mo/pQq1Yt2rVrFz19+pQkSaKjR4/SnTt35Mk4IiKCsrKyIHW1m5sb+fr66p26WkzaAoHg00GiPMoj2Yt7eHiQra2t/AoNDf1HXzt//nwqU6YMubu7k6mpKTVp0oQWL15MtWrVIqLsRHqmpqZUsCAGvzk7O8tprfOK8B4RCASfDnr6aUdFRYE8YmZm9q5PvJf58+fTmTNnaNeuXeTl5UXHjx+n/v37k6urKzVo0OCdn8stdfXbMNhJO9VZQcZ/a9keBzkUu9oeTIXaw0JLb8ZKW9Rr2FDoF4ljfS5pKGqQLyNZG8+wwZ2Yqka9ObMg64rWD1BXtH2AJ8zVWNatjw1Djb2lHYfkH+0yA2z3slBn67eWK2336ngAbHfCveV2SHn8J7dfgSWyhs5gP+k1e1uirchBuf1jSBuwFdqKN2VDxnFKg/GXcD23rNgvuuHlwWDzcMT99aICn4KaWJ00pIVQ4y5fgJ+0F3V4Drb5xwrJ7eRIN7CZWeAxKbGe05Qme+JzkJo/npHb9krUuy2e461zibn8vOJgGJ58FetwBfh7RXA8g89h/EDhq3xeqgPxvGzidgP6m9dwZXeXM5hu9XYF/h7XM2Aiu2u4L2O/5PNdmY77J9Gbj3WKlm6uztQJsTdENJS3sjR/P9b4EJlF09LSaOzYsbR9+3Zq3rw5ERGVL1+eLl++TDNnzqQGDRqQi4sLZWZmUlxcHFxtx8bGvjcL6tsQ8ohAIPhk+K9c/t5HVlYWZWVlkZERTqfGxsak0WT/O1SuXJlMTEwgdXV0dDRdu3ZN70nbYK+0BQKBQG/+ozD25ORkunfvntyPjIyky5cvk729PXl6epK/vz+NGDGCLCwsyMvLi8LDw+mXX36h2bNnExGRra0t9e7dm4YNG0YODg5kb29Pw4cPp3Llyr1XPnkbBjtpa0wlIrPsHfuyEt/ix6nQTa22Obup1Tw6CGzFYvB2duq6FXJ7yMhAsC0OXSO3z9YuBrZ117F6dtEtLI/EVsZdaBOFlXUqj+Sw5IaTsLpJcke+/f7hfkew3bzpDn3tgj1HuuJ4fBeyi92N5y5gM07DkOk1Dzk8vukIrI6j7XpWrfVVsD0//25fUlU6uubd2V5CbtfteAVsj5JLQL9Ne76Pr2z1EGw/bvgG+v+LKyK3jXA3k3kx3s5vx2MWxIfpmFXvgVaWPXU6yiMnfubK7fs7Ywh5Uh+8kor+jSvkqHzxXDt/jc+h0CGYHXPcuTbQN1LxBOJqHQ+2OynoLphUmjc8ywbHXnI5y4hpzuiyGdUMq9cranLGRNURlNQySnF4/nedw+V2erKKrq4mw0aTx3pjGv2qIFy4cIHq1mVX3zfVtnr06EFr1qyhTZs20ZgxY6hr1670+vVr8vLyoqlTp1Lfvixrzpkzh5RKJXXs2JHS0tKofv36tGbNGjI21k92MthJWyAQCPRGT007rwQEBND7QlpcXFxo9er3/6OZm5vTggULaMGCBfp9uQ5i0hYIBJ8MonKNQCAQ5Cc+g9SsBjtpF63xmEyssl3tXC04tvcLHd2z9ubhcrtfE6x8sskeq3LviOe+xQsURWf15jqWL31RDzTT8QhKHcHuZt09LuN3RuJ3hu3mNKAWSjxR2pfgzyar0T/0+V9YRT2+FuuMY77fCLafBv8gt6VKeEgTvPE7Q0pwLoRRs78H2+oR8+T21zsHgq35OqzmczieXRl7ffEn2LZcZLe0v1Zi2tgflmDFmZk7Wsvtg/dqgq1VIEaKlbVgl7/Vg9uALb4/Px849horzDxJtoP+q2R+LjK9wlawDa78rdxWR2OofIH7uG9TK/Ix2V8bc1LMj+V9MOtndPGzbInphZNdOez60UUfsJk/R72z+An+TuNETAP8cAxr7k6/gokGfIv7fcsADpu2mXgXbDcP8Riq1GAX2xR1PqiGq8ljFYR8nOXPYCdtgUAg0BtxpS0QCAT5ibxm8BOT9gdHqdCQ8m/XnW6F+PZ74DzMEmf0JUeFPUpD165uRc9Bf9mN2nLbIxNd2OLH8a1mYSvMBXD7XBHop2ZwhOTvjzASblRJjFacuYVll/kT8Ba67xyWIEwaYySc9VfR0I+7ya58Q6f2A1vC13zL/E3Zk2Bbe6I29EfOZ0kk0x5M1Pm4VoHegplga2CLhWKH7uwut4c02ws2SevpfZojPspfPakV9Mv351vza8VcwdbWFiWZrjsHyO2xc3eAbcoxXu/pGJQ1Si5HGcFtPu/bBzpVbRoFXJLb+89UAJtJMv7Qy3lwEedjqShrHHnE/S++uwm2c8fQldD9EsslL6thBKtjbTwP1OfYdS+yi07FGW8eu/fUF2Db0aMe9O9/yz99owvo4lqnGbt7nk1lW3qaiogwItngEFfaAoFAkI/QSJSnq2ihaQsEAoEBoFETUR6KCmj0KzxgSIhJWyAQfDqIK+2PR0KGOSmV2W5wY0ew1lowDV31an9zS26HPUBXL48yr6Gf/opd+YrPw/DqE0+48snDE95gcwpAjTvhGOvLFtdUYHs9HTXJEn1Yz5z5FKtT9O3Hblg7O6P2nOGC+rz0FV8ZxNfD7ywYxttlWwH12w41sZLNbhdfXuc11EQ9XXl/KUNQ8A5K7AZ9syR2L5t1viHY7OpzRrkV5daDbdDNr6F/fxOHtfftj5Xav12LbodNW7DG/TgTw7IXNuTK6Sue+oPt0Zeo2Sr6s+ta0a3oVrjtWUW57V0a9eT046i533nBmQUfxOF3apOqU4Fn0lcY1n6tGacsiFmB58Grx/id5Sbw+W6js979N9kNs0cFTPOXPjUJ+j4T2ZWwzPxrYDs9l91Un0TyvlOp0onoMBk0n4GmrXeWv6dPn9I333xDDg4OZGlpSRUrVqSICP4xSZJEwcHB5ObmRhYWFhQQEEDXr19/zxoFAoHgA6FnEYT8iF6TdlxcHNWsWZNMTExo3759dOPGDZo1axbZ2dnJy0yfPp1mz55NCxcupPPnz5OLiws1bNiQkpKS3r1igUAg+BD8R4V9DQm95JGff/6ZPDw8IDFKkSJF5LYkSTR37lwaN24ctWvXjoiI1q5dS87OzrRhwwbq06eP7ioFAoHgw6HRUJ6yQemZ5c+Q0GvS3rVrFzVu3Jg6dOhA4eHhVLhwYerfvz99/322729kZCTFxMRA8UozMzPy9/enU6dO6TVpB3odIUvrbN1teNUe8vuSJ6bAnOXIlWs8zFHD3jMefVN/DGEN+WQC+tVabrOV2/ZX4sCWFIBjK3ib9eWf5y8BW4zaFvpXN7POqNB5YP36dhG5HV8T9UmL1liZZZQna9P1rO6Abawnh4L/ct8PbA5WqHGrb7IPc1YR3JdOlnw3dLsiVlspfBQH/1IrOt3REe+i0rP4tBowCdPlxjVMg765lku17tiLbMXjud+mktxW2+OzjQhvT7kdm4LPFb7qcwT6KyuwbmyuwPVkLeXnFQUGPAFbZC30OY/wWy6368wfDraizSPl9o0YTJe7eHYH6Cd48f6SdAqJG+vUmb2yv5TcLngbJx5HCx7f8bXVwZZQGtMkxHfVquI0swrYCn7/WG7fu8RV3TXpRHSaDBuhaSMPHjygJUuWkI+PDx04cID69u1LgwYNol9+yX4I9KZApbMzBiy8r3hlRkZGjorIAoFA8I8Q8gii0WioSpUqFBISQkRElSpVouvXr9OSJUuoe3eOkNMtVPm+4pWhoaE0ceJEfcctEAgEOREuf4irqyuVKVMG3itdujRt3ZqdKc3FJfs2MCYmhlxd2VUpNjY2x9X3G8aMGSNXgSAiSkxMJA8PDxoV3omMLLLvFYe32iPbozPt4PMrX9aR21emYdixMgNvH9cN4wK0PWdj1rPwqrxdKW3xBmRDqQ3Q7+U4RG5/vwTd0rKqolRQdF+s3NYsQWlAM47d+kKGrQPbrQx09Zp5iSWn1Ap4q2tuzLe6ZRxRVnkWXBz6pcdxGLKnFcoP4evZ1SupKMohFo2wMKzpAa6oYqSTVU36kyuhzP0JQ/eDbmDGO+cmPN6YX4uAre3veIweZ7Cb36YbmE0x4yeWIMx/jAfblhUok3nf5hD9gfbogpjRlm2W+zHTYsP2l6Df7nsuWvzDLAzlL6TkO8YVk9uBLckDM/cVvMsSjff4W2C7Nb+szmf53EztGg+2rFPspmn5As/hDDvokutxtls+x5QFY4vwtmR58VhTktTUdiwZNJJGTZKUe+BMXpYxVPSSR2rWrEm3b9+G9+7cuUNeXtknt7e3N7m4uEDxyszMTAoPD39n8UozMzO5IvKHqIwsEAg+Y4Q8ggwZMoRq1KhBISEh1LFjRzp37hwtX76cli/PfiCjUCgoKCiIQkJCyMfHh3x8fCgkJIQsLS2pS5cuuaxdIBAI/iV5rREpfSbeI1WrVqXt27fTmDFjaNKkSeTt7U1z586lrl27ysuMHDmS0tLSqH///hQXF0d+fn508OBBsra2fs+aBQKB4AMg5VHTzsdX2grpfdUqPwKJiYlka2tL9ff2IaVVtnb76DVrpOlpprC84wH2kXpZCTfl63pYUeVeCocd3/qtFNiSvfifd23bxWD75mBf6BtbswZZ4CxWuZk7eCn0+67nz1p9gelXX7/kP7LqJTDlpUpC5epHd9YZWx9AHd1zDz/kjdWpXOMcgS5taQ5snx28CGyDfubUp1lW+OA4oyDu2ywt10sjnYo83XzZPdHLDLd5zdDW0E8txONJxGhzcjuOWmtUL9Yhje7jfs8sxDZlImrGRbelQD+uJFcxTyqC25lVkl0kf/wCw+oXTf8K+q/q8PgK78T9Xns8+8bt2FILbPa10ZOqkwdHFGdJOPatTypC/9kz1q0Vxni1aH6Pfwv2N1Czja2K55NRUa70U9geK+mYjGG31buDebs0qen0+PvJlJCQYHAy5pt5o55lZ1IqTHNdXiVl0pHUTQa5LblhsLlHBAKBQG8+gyttMWkLBIJPh7zWiBST9odnvOceKmCdfUvX/iK7VhU+gbeELyry7a2dD7ql7V+At6VDRm6W2y+vFgGbRVO+Rew/PxBsNjrHN7ECj6FMJ6xKMr1JG+gXWfZIbn/vfgJs1zw5u9ueeZglLh7VG2ptz5kHS6xMB5vJdJYgVnn/DrYGHkOg71eGvX/WvMCMctoVZ8p8ha5nF09gBkVK5ghORUEM22uvVXFmyYsAsLmMuw/94lZcYSVRheGAR5OrQn9yZS5o/Ic3Fgw20Xr49CIDIyJVa/D2N9mD7enuKB81KMaVdNb3awG2lx1QcihUiN36FFJBsG36kyMSpeK4f57F4LK7prJLYkJR3Ae68o1JKS2pJ9ISbAVv8fhelUOZpVlDrOL0xx12JXQwR/no/ngeg91u3ldqVKsME0miPIWxi0lbIBAIPj6SRiIpD1faBvYoTy/EpC0QCD4ZJLWaJEXu4Sf5ObjG4CbtN/+AKcl8i6NJZzlAlYW3Pup03gR1Kt6GKjJRRkhL5sjB7ITuWutJ4c+qM9Cm+1xDk8b3iVkpeM+oUuMYNFrrTU3CEyUjnW/N1Tpj1egMQaNV/EGl0skipDWG5CTcP5o0XJH2eDONcezaY9DdLu1jQESkUfL3KMxwPNpjyEzWSciUhevNkNieqcJbet3jkJrM+093fJLWLXFWps526RwT7fVqdIpqZCbzZ3XPEU0a/ly0zzdVlu6yLGtIRli0Qjejg0rFY9eVINTpuLAmVet70nFy0v5tqNNxX+oeB+316O5L+B1pnRNvzg9DvkpVSRl58sFWUVauyxgqBufy9+TJE/Lw8Mh9QYFA8FGIiooid3f33Bf8fyQ9PZ28vb3fmZjubbi4uFBkZCSZm5vnvrABYXCTtkajoWfPnpEkSeTp6UlRUVH5zo/y/4M3OVrE/nk3Yh+9H333jyRJlJSURG5ubmRkpHfRq/+c9PR0yszM+9NSU1PTfDdhExmgPGJkZETu7u5yilaRj+T9iP2TO2IfvR999o+trW3uC30kzM3N8+UkrC+G93cpEAgEgnciJm2BQCDIRxjspG1mZkYTJkwgMzOz3Bf+DBH7J3fEPno/Yv/kTwzuQaRAIBAI3o3BXmkLBAKBICdi0hYIBIJ8hJi0BQKBIB8hJm2BQCDIRxjspL148WLy9vYmc3Nzqly5Mp04cSL3D32ChIaGUtWqVcna2pqcnJyoTZs2OYorS5JEwcHB5ObmRhYWFhQQEEDXr1//SCP+eISGhsp1St8g9g3R06dP6ZtvviEHBweytLSkihUrUkQEp88V+yh/YZCT9m+//UZBQUE0btw4unTpEtWuXZuaNm1Kjx8//thD+38nPDycBgwYQGfOnKGwsDBSqVTUqFEjSknhHMjTp0+n2bNn08KFC+n8+fPk4uJCDRs2pKSkpI848v9fzp8/T8uXL6fy5cvD+5/7vomLi6OaNWuSiYkJ7du3j27cuEGzZs0iOzs7eZnPfR/lOyQDpFq1alLfvn3hvVKlSkmjR4/+SCMyHGJjYyUiksLDwyVJkiSNRiO5uLhI06ZNk5dJT0+XbG1tpaVLl36sYf6/kpSUJPn4+EhhYWGSv7+/NHjwYEmSxL6RJEkaNWqUVKtWrXfaxT7KfxjclXZmZiZFRERQo0aN4P1GjRrRqVOnPtKoDIeEhOwKO/b22QVeIyMjKSYmBvaXmZkZ+fv7fzb7a8CAAdS8eXNq0KABvC/2DdGuXbuoSpUq1KFDB3JycqJKlSrRihUrZLvYR/kPg5u0X758SWq1mpydneF9Z2dnvdIufopIkkRDhw6lWrVqka+vLxGRvE8+1/21adMmunjxIoWGhuawfe77hojowYMHtGTJEvLx8aEDBw5Q3759adCgQfTLL78QkdhH+RGDy/L3BoVOpnhJknK897kRGBhIV65coZMnT+awfY77KyoqigYPHkwHDx58b3a3z3HfvEGj0VCVKlUoJCSEiIgqVapE169fpyVLllD37t3l5T7nfZTfMLgrbUdHRzI2Ns7xLx8bG5vjauBzYuDAgbRr1y46evQoJKB3cXEhIvos91dERATFxsZS5cqVSalUklKppPDwcJo/fz4plUp5+z/HffMGV1dXKlOmDLxXunRp+aH+53z+5FcMbtI2NTWlypUrU1hYGLwfFhZGNWrU+Eij+nhIkkSBgYG0bds2OnLkCHl7e4Pd29ubXFxcYH9lZmZSeHj4J7+/6tevT1evXqXLly/LrypVqlDXrl3p8uXLVLRo0c9237yhZs2aOVxE79y5Q15eXkT0eZ8/+ZaP+RT0XWzatEkyMTGRVq5cKd24cUMKCgqSrKyspIcPH37sof2/069fP8nW1lY6duyYFB0dLb9SU1PlZaZNmybZ2tpK27Ztk65evSp9/fXXkqurq5SYmPgRR/5x0PYekSSxb86dOycplUpp6tSp0t27d6Vff/1VsrS0lNavXy8v87nvo/yGQU7akiRJixYtkry8vCRTU1Ppiy++kF3cPjcou6xwjtfq1avlZTQajTRhwgTJxcVFMjMzk+rUqSNdvXr14w36I6I7aYt9I0m7d++WfH19JTMzM6lUqVLS8uXLwS72Uf5CpGYVCASCfITBadoCgUAgeDdi0hYIBIJ8hJi0BQKBIB8hJm2BQCDIR4hJWyAQCPIRYtIWCASCfISYtAUCgSAfISZtgUAgyEeISVsgEAjyEWLSFggEgnyEmLQFAoEgHyEmbYFAIMhH/B+xb+NiuY+TegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_visualiztion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "7343f851",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_ind = np.random.choice(range(len(val_dataset)), size=10, replace=False)\n",
    "for ind in rand_ind:\n",
    "    visualize(val_dataset[ind][0], val_dataset[ind][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "fb04216d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(predicted, labels, conf_matrix):\n",
    "    for p, t in zip(predicted, labels):\n",
    "        conf_matrix[p, t] += 1\n",
    "    return conf_matrix\n",
    "#首先定义一个 分类数*分类数 的空混淆矩阵\n",
    "conf_matrix = torch.zeros(classes_num, classes_num)\n",
    "# 使用torch.no_grad()可以显著降低测试用例的GPU占用\n",
    "with torch.no_grad(): # 停止梯度更新\n",
    "    for i, (images, labels) in enumerate(val_dataloader):\n",
    "        images = images.to(device)\n",
    "        images = torch.sqrt(torch.squeeze(images))\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        out_labels, out_images = model(images)\n",
    "        _, predicted = torch.max(out_labels.data, 1)\n",
    "        \n",
    "        #记录混淆矩阵参数\n",
    "        conf_matrix = confusion_matrix(predicted, labels, conf_matrix)\n",
    "        conf_matrix = conf_matrix.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "d4cd8a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵总元素个数：10000,测试集总个数:10000\n",
      "[[ 961.    0.   18.   12.    1.   15.   33.    5.   27.   28.]\n",
      " [   0. 1095.    1.    3.    2.    2.    2.   16.    9.    7.]\n",
      " [   0.    3.  909.   27.    8.    7.    3.   26.   16.    6.]\n",
      " [   2.    3.   12.  880.    0.   36.    0.    6.   32.   14.]\n",
      " [   0.    1.   14.    1.  900.    5.    5.   13.   17.   49.]\n",
      " [   3.    1.    3.   21.    0.  763.   16.    2.   20.    8.]\n",
      " [   7.    9.   21.   12.   15.   25.  897.    5.   19.    1.]\n",
      " [   1.    2.   22.   32.    1.   14.    0.  922.   19.   39.]\n",
      " [   6.   21.   31.   16.   12.   18.    2.    2.  804.    8.]\n",
      " [   0.    0.    1.    6.   43.    7.    0.   31.   11.  849.]]\n",
      "每种标签总个数：1100  1137  1005  985  1005  837  1011  1052  920  948\n",
      "每种标签预测正确的个数：961  1095  909  880  900  763  897  922  804  849\n",
      "每种标签的识别准确率为：87.4%  96.3%  90.4%  89.3%  89.6%  91.2%  88.7%  87.6%  87.4%  89.6%"
     ]
    }
   ],
   "source": [
    "conf_matrix = np.array(conf_matrix)# 将混淆矩阵从gpu转到cpu再转到np\n",
    "corrects = conf_matrix.diagonal(offset = 0)#抽取对角线的每种分类的识别正确个数\n",
    "per_classes = conf_matrix.sum(axis = 1)#抽取每个分类数据总的测试条数\n",
    "\n",
    "print(\"混淆矩阵总元素个数：{},测试集总个数:{}\".format(int(np.sum(conf_matrix)), BATCH_SIZE*len(val_dataloader)))\n",
    "np.set_printoptions(suppress=True) \n",
    "print(conf_matrix)\n",
    "\n",
    "# 获取每种label的识别准确率\n",
    "percent = [rate*100 for rate in corrects/per_classes]\n",
    "per_classes = list(per_classes)\n",
    "corrects = list(corrects)\n",
    "\n",
    "print(\"每种标签总个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in per_classes])))\n",
    "print(\"每种标签预测正确的个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in corrects])))\n",
    "print(\"每种标签的识别准确率为：{}\".format('%  '.join(['{:.1f}'.format(i) for i in percent])), end='%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "46a9d760",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "label_ticks = list()\n",
    "for i in range(classes_num):\n",
    "    label_ticks = label_ticks + ['{}'.format(classes[i])]\n",
    "\n",
    "# 显示数据\n",
    "plt.imshow(conf_matrix, cmap=plt.cm.Blues)\n",
    "thresh = conf_matrix.max() / 2\t#数值颜色阈值，如果数值超过这个，就颜色加深。\n",
    "for i in range(classes_num):\n",
    "    for j in range(classes_num):\n",
    "        info = int(conf_matrix[j, i])\n",
    "        plt.text(i, j, info,\n",
    "                 verticalalignment='center',\n",
    "                 horizontalalignment='center',\n",
    "                 color=\"white\" if info > thresh else \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "85bd5d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将模型中的phase导出来\n",
    "phase = []\n",
    "for param in model.named_parameters():\n",
    "    phase.append(param[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "0c82e980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 64])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "2e4d0f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 写一个测试模型，用于查看中间层的输出图案\n",
    "class DNN_test(torch.nn.Module):\n",
    "    def __init__(self, phase=[], Amp = [], num_layers=5, wl = 532e-9, N_pixels = 64, pixel_size = 10*wl, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN_test, self).__init__()\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        E_out = []\n",
    "        Int = []\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*torch.pi*torch.sigmoid(self.phase[index])\n",
    "            constr_phase = 2*torch.pi*phase[index]\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase * torch.sigmoid(Amp[index])\n",
    "            E_out.append(E)\n",
    "        E_out.append(self.last_diffractive_layer(E_out[-1]))\n",
    "        for i in range(len(E_out)):\n",
    "            Int.append(torch.abs(E_out[i])**2)\n",
    "        return Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "45221648",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_test = DNN_test(phase = phase, Amp = Amp, num_layers = 5, wl = wl, pixel_size = pixel_size, distance = distance).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "a08c96aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mid_img = model_test(images_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "66f84499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "cead7a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mid_img[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "9b954ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165172822b0>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品的原图\n",
    "plt.imshow(images_E[1].cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "7b6e861a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165178afcd0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品在第1层的图案\n",
    "plt.imshow(mid_img[5][1].cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b551ae2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "D2NN",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
